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1. Using Distributed Acoustic Sensing to Monitor and Investigate Eruptive Events at Stromboli Volcano, Italy

Author

Francesco Biagioli, Jean-Philippe Métaxian, Eléonore Stutzmann, Maurizio Ripepe, Alister Trabattoni, Pascal Bernard, Roberto Longo, Gianluca Diana, Lorenzo Innocenti, Yann Capdeville, Marie-Paul Bouin, and Giorgio Lacanna

Abstract

Volcano seismology is essential for understanding, monitoring, and forecasting eruptive events. The use of distributed acoustic sensing (DAS) technology can be particularly useful for this purpose because of its high temporal and spatial resolution, which may help to overcome the challenges of deploying and maintaining seismic arrays on volcanoes.Between 2020 and 2022, we installed 4 km of optical fibre on Stromboli volcano, Italy, whose persistent activity is well-suited for investigating the related dynamic strain rate. The cable was buried at a depth of 30 cm and the layout geometry was designed to provide wide coverage while being constrained by natural obstacles and topographical features. Seismometers were also installed along the fibre. DAS data were collected using a Febus A1-R interrogator, and the acquisition period increased from one week in 2020 to over four months in 2022. We recorded volcanic tremor, ordinary explosions (several per hour), two major explosions in 2021 and 2022, and the entire sequence of a pyroclastic flow in 2022. DAS and seismic data show good agreement in both time and frequency domains after converting strain rate to velocity and vice versa using different methodologies. Beamforming of DAS data shows a dominant signal in the 3-5 Hz frequency band coming from the active craters. We will also present preliminary results of major explosions and pyroclastic flow. This experiment demonstrates that DAS can be used for monitoring volcanic activity.

Published
2023
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2. The Merapi Volcano Monitoring System

Author

Agus Budi-Santoso, François Beauducel, I Gusti Made Agung Nandaka, Hanik Humaida, Fidel Costa, Christina Widiwijayanti, Masato Iguchi, Jean-Philippe Métaxian, Indra Rudianto, Much Rozin, null Sulistiyani, Ilham Nurdin, Karim Kelfoun, Svetlana Byrdina, Virginie Pinel, Ali Ahmad Fahmi, Antoine Laurin, Mochammad Husni Rizal, and Nabil Dahamna

Published
2023
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3. Imaging of a magma system beneath the Merapi Volcano complex, Indonesia, using ambient seismic noise tomography

Author

Ahmad Ali Fahmi, Sri Widiyantoro, Nicholas Rawlinson, Z. Zulfakriza, Massita Putriastuti, Jean-Philippe Métaxian, Andri Dian Nugraha, Tedi Yudistira, François Beauducel, Agus Budi-Santoso, Antoine Laurin, Rawlinson, Nicholas [0000-0002-6977-291X], Apollo - University of Cambridge Repository, Institut des Sciences de la Terre (ISTerre), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-Université Grenoble Alpes (UGA), Institut de Physique du Globe de Paris (IPGP (UMR_7154)), and Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)

Subjects
geography, geography.geographical_feature_category, Asia, 010504 meteorology & atmospheric sciences, Seismic noise, Volcanic structure, 010502 geochemistry & geophysics, 01 natural sciences, Geophysics, Volcano, [SDU]Sciences of the Universe [physics], Geochemistry and Petrology, Magma, Tomography, Petrology, Geology, 0105 earth and related environmental sciences
Abstract

SUMMARYMt Merapi, which lies just north of the city of Yogyakarta in Java, Indonesia, is one of the most active and dangerous volcanoes in the world. Thanks to its subduction zone setting, Mt Merapi is a stratovolcano, and rises to an elevation of 2968 m above sea level. It stands at the intersection of two volcanic lineaments, Ungaran–Telomoyo–Merbabu–Merapi (UTMM) and Lawu–Merapi–Sumbing–Sindoro–Slamet, which are oriented north–south and west–east, respectively. Although it has been the subject of many geophysical studies, Mt Merapi's underlying magmatic plumbing system is still not well understood. Here, we present the results of an ambient seismic noise tomography study, which comprise of a series of Rayleigh wave group velocity maps and a 3-D shear wave velocity model of the Merapi–Merbabu complex. A total of 10 months of continuous data (October 2013–July 2014) recorded by a network of 46 broad-band seismometers were used. We computed and stacked daily cross-correlations from every pair of simultaneously recording stations to obtain the corresponding inter-station empirical Green's functions. Surface wave dispersion information was extracted from the cross-correlations using the multiple filtering technique, which provided us with an estimate of Rayleigh wave group velocity as a function of period. The group velocity maps for periods 3–12 s were then inverted to obtain shear wave velocity structure using the neighbourhood algorithm. From these results, we observe a dominant high velocity anomaly underlying Mt Merapi and Mt Merbabu with a strike of 152°N, which we suggest is evidence of old lava dating from the UTMM double-chain volcanic arc which formed Merbabu and Old Merapi. We also identify a low velocity anomaly on the southwest flank of Merapi which we interpret to be an active magmatic intrusion.

Published
2021
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4. A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière Volcano, Guadeloupe

Author

Jean-Christophe Komorowski, Jean-Bernard de Chabalier, Alexis Falcin, Roberto Moretti, G. Ucciani, Arnaud Lemarchand, Jean-Philippe Métaxian, Jerome Mars, Céline Dessert, François Beauducel, Jean-Marie Saurel, Eléonore Stutzmann, Marielle Malfante, Arnaud Burtin, Institut de Physique du Globe de Paris (IPGP (UMR_7154)), Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut des Sciences de la Terre (ISTerre), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-Université Grenoble Alpes (UGA), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Grenoble Alpes (UGA), Observatoire Volcanologique et Sismologique de Guadeloupe (OVSG), Institut de Physique du Globe de Paris (IPG Paris), Département d'Architectures, Conception et Logiciels Embarqués-LIST (DACLE-LIST), Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut de Recherche pour le Développement (IRD), Institut de Physique du Globe de Paris (IPGP), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Institut de Physique du Globe de Paris, Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Falcin, A., Metaxian, J. -P., Mars, J., Stutzmann, E., Komorowski, J. -C., Moretti, R., Malfante, M., Beauducel, F., Saurel, J. -M., Dessert, C., Burtin, A., Ucciani, G., de Chabalier, J. -B., and Lemarchand, A.

Subjects
010504 meteorology & atmospheric sciences, Seismology Volcano La Soufrière Classification Machine Learning, Class (philosophy), 010502 geochemistry & geophysics, 01 natural sciences, Machine Learning, Geochemistry and Petrology, Cepstrum, Feature (machine learning), [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, Entropy (energy dispersal), Volcano, Seismology, 0105 earth and related environmental sciences, Event (probability theory), business.industry, Pattern recognition, Classification, La Soufrière, Random forest, Data set, Geophysics, Modal, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Geology
Abstract

International audience; The classification of seismo-volcanic signals is performed manually at La Soufrière Volcano, which is time consuming and can be biased by subjectivity of the operator. We propose here a machine-learning-based model for classification of these signals, to handle large datasets and provide objective and reproducible results. To describe the properties of the signals, we used 104 statistical, entropy, and shape descriptor features computed from the time waveform, the spectrum, and the cepstrum. First, we trained a random forest classifier with a dataset provided by the Observatoire Volcanologique et Sismologique de Guadeloupe that consisted of 845 labeled events that were recorded from 2013 to 2018: 542 volcano-tectonic (VT); 217 Nested; and 86 long period (LP). We obtained an overalll accuracy of 72%. We determined that the VT class includes a variety of signals that cover the VT, Nested and LP classes. After visual inspection of the waveforms and spectral characteristics of the data set, we introduced two new classes: Hybrid and Tornillo. A new random forest classifier was trained with this new information, and we obtained a much better overall accuracy of 82%. The model is very good for recognition of all event classes, except Hybrid events (67% accuracy, 70% precision). Hybrid events are often considered to be a mix of VT and LP events. This can be explained by the nature of this class and the physical processes that include both fracturing and resonating components with different modal frequencies. By analyzing the feature weights and by training a model with the most important features, we show that a subset of the 14 best features is sufficient to obtain a performance that is close to that of the model with the whole feature set. However, these best features are different from the 13 best features obtained for another volcano in Peru, with only one feature common to both sets of best features. Therefore, the model is not universal and it must be trained for each volcano, or it is too specific to the one station used here.

Published
2021
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6. Simulation of Topography Effects on Rockfall‐Generated Seismic Signals: Application to Piton de la Fournaise Volcano

Author

Luis Fabian Bonilla, Julian Kuehnert, Clément Hibert, Emmanuel Chaljub, Anne Mangeney, Philippe Kowalski, Frédéric Lauret, Yann Capdeville, Christophe Brunet, Jean-Philippe Métaxian, Patrice Boissier, Eléonore Stutzmann, Institut de Physique du Globe de Paris (IPGP), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Laboratoire de Planétologie et Géodynamique [UMR 6112] (LPG), Université d'Angers (UA)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Université Gustave Eiffel, Institut des Sciences de la Terre (ISTerre), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-Université Grenoble Alpes (UGA), Ecole et Observatoire des Sciences de la Terre (EOST), Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut de Physique du Globe de Paris (IPGP (UMR_7154)), and Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)

Subjects
geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], Spectral element method, 01 natural sciences, Seismic wave, Physics::Geophysics, Geophysics, Rockfall, Volcano, 13. Climate action, Space and Planetary Science, Geochemistry and Petrology, Position (vector), Earth and Planetary Sciences (miscellaneous), Soil properties, Geology, Seismology, 0105 earth and related environmental sciences
Abstract

Seismic waves generated by rockfalls contain valuable information on the properties of these events. However, as rockfalls mainly occur in mountainous regions, the generated seismic waves can be affected by strong surface topography variations. We present a methodology for investigating the influence of topography using a Spectral-Element-based simulation of 3-D wave propagation in various geological media. This methodology is applied here to Dolomieu crater on the Piton de la Fournaise volcano, Reunion Island, but it can be used for other sites, taking into account local topography and medium properties. The complexity of wave fields generated by single-point forces is analyzed for different velocity models and topographies. Ground-motion amplification is studied relative to flat reference models, showing that Peak Ground Velocity (PGV) and total kinetic energy can be amplified by factors of up to 10 and 20, respectively. Simulations with Dolomieu-like crater shapes suggest that curvature variations are more influential than depth variations. Topographic effects on seismic signals from rockfalls at Dolomieu crater are revealed by interstation spectral ratios. Results suggest that propagation along the topography rather than source direction dominates the spectral ratios and that resulting radiation patterns can be neglected. The seismic signature of single rockfall impacts is studied. Using Hertz contact theory, impact force and duration are estimated and then used to scale simulations, achieving order-of-magnitude agreement with observed signal amplitudes and frequency thresholds. Our study shows that combining Hertz theory with high-frequency seismic wave simulations on real topography improves the quantitative analysis of rockfall seismic signals.

Published
2020
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7. Crustal thickness beneath Mt. Merapi and Mt. Merbabu, Central Java, Indonesia, inferred from receiver function analysis

Author

Mohamad Ramdhan, Nicholas Rawlinson, Jean-Philippe Métaxian, S. Widiyantoro, Agus Budi-Santoso, S K Suhardja, Rawlinson, Nicholas [0000-0002-6977-291X], and Apollo - University of Cambridge Repository

Subjects
geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Physics and Astronomy (miscellaneous), Crustal Thickness, Receiver Function, Partial melting, Astronomy and Astrophysics, Crust, sub-02, Structural basin, 010502 geochemistry & geophysics, 01 natural sciences, Geophysics, Amplitude, Discontinuity (geotechnical engineering), Volcano, Space and Planetary Science, Receiver function, Indonesia, Isostasy, Petrology, Geology, Mt. Merapi, 0105 earth and related environmental sciences
Abstract

In this study, we analysed 2708 receiver functions (RFs) using data recorded by 53 seismographic stations that surround Mt. Merapi and Mt. Merbabu – two volcanos in Central Java - to map the boundary between Earth's crust and upper mantle. We observe that a number of RFs from this new dataset have complex signals and do not exhibit typical RF characteristics; in particular, where the converted Ps signal from the Moho discontinuity is the clearest and strongest amplitude arrival following the P onset. This effect may be related to complex shallow velocity structure due to the presence of magmatic rocks and sediments. Further analysis of the RF results using the H-κ method suggests that Moho depth varies between 27 and 32 km beneath the array, with no apparent correlation between crustal thickness and surface topography, as one might expect from Airy isostacy. For instance, the Moho is quite shallow beneath Mt. Merapi (up to 27 km depth), despite its elevation of nearly 3 km. This may be a consequence of dynamic support from an active upper mantle coupled with erosion and/or weakening of the lower crust due to the active volcanic plumbing system. To the north of Mt. Merapi, the Moho is deeper (30–31 km depth) below Mt. Merbabu. Vp/Vs ratio estimates from the H-κ method are relatively high (~1.9) beneath the Mt. Merapi and Kendeng Basin area, which may indicate the presence of a zone of hydrous and active partial melting in the underlying crust. Lower Vp/Vs ratios (~1.7) are found beneath Mt. Merbabu, which may be due to its relative lack of volcanic activity compared to Mt. Merapi.

Published
2020
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8. Simulation of rockfall generated seismic signals and the influence of surface topography

Author

Clément Hibert, Emmanuel Chaljub, Luis Fabian Bonilla, Yann Capdeville, Julian Kuehnert, Anne Mangeney, Eléonore Stutzmann, Christophe Brunet, Frédéric Lauret, Philippe Kowalski, Jean-Philippe Métaxian, and Patrice Boissier

Subjects
geography, Rockfall, geography.geographical_feature_category, Event (relativity), Geology, Seismic wave, Seismology
Abstract

Rockfalls seismic waves contain valuable information on event properties. However, as rockfalls predominately occur in mountainous regions, generated seismic waves are prone to be affected by stron...

Published
2020
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9. Automatic classification of seismo-volcanic signals at La Soufrière of Guadeloupe

Author

Eléonore Stutzmann, Jean-Philippe Métaxian, Jerome Mars, Roberto Moretti, Jean-Christophe Komorowski, and Alexis Falcin

Subjects
geography, geography.geographical_feature_category, Volcano, Geology, Seismology
Abstract

Seismic activity at La Soufrière volcano of Guadeloupe is composed of various transient signals, which are classified manually by the Observatoire Volcanologique et Sismologique de Guadeloupe (OVSG-IPGP) considering waveforms recorded at several stations. Although five main types of signals are recognized in the data analysis by the observatory (Moretti et al., 2020), only three main classes readily distinguishable on seismic traces during the daily analytical protocol have been catalogued: Volcano-Tectonic events, Long-Period events and Nested events, each related to a distinct physical process.Automatic classification of seismo-volcanic signals of La Soufrière was performed by using an architecture based on supervised learning, available at github.com/malfante/AAA. Seismic waveforms are transformed into a large set of features (34 features for each representation domain) computed from three representation domain of the signal (time, frequency, quefrency). The resulting vectors of features are then used for the modeling. We are using the Random Forest Classifier algorithm from the scikit-learn library.At first, we trained the model with the dataset given by the OVSG consisting of 845 available labeled events (542 VT, 217 nested and 86 LP) recorded in the period 2013-2018. We obtained an average classification rate of 72 %. We determined that the VT class includes a variety of signals covering the LP, Nested and VT classes. Reviewing in details the waveforms and the spectral characteristics of the signals belonging to the 3 classes we then introduced Hybrid events and also defined a monochromatic class (so-called Tornillo) of LP signals, thus matching the full description of signals provided in Moretti et al. (2020).Then, using the new information, a new model was trained with 5 classes and tested. We obtained a much better classification average rate of 84 %. The classification is excellent for Nested events (93 % of accuracy and precision) and Tornillo events (93% of accuracy and precision). The classification of VT events (90% accuracy, 89% precision) and LP events (86% accuracy, 82% precision) were also very good. The most difficult class to recognize is the Hybrid class (64 % accuracy, 69 % precision). Hybrid events are often mixed with VT and LP events. This may be explained by the nature of this class and the physical process that includes both a fracturing and a resonating component with different modal frequencies.Machine learning is a powerful tool to handle large datasets. From a dataset built manually, the processing we applied allowed to obtain a reliable automatic classification by refining class definitions. This has important implications for observatory data processing during unrest and eruptive activity.

Published
2020
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10. Early Results of Time Domain Receiver Function Data Processing in Mt Merapi and Mt Merbabu

Author

A Abdullah, S K Suhardja, Mohamad Ramdhan, Andri Dian Nugraha, Z. Zulfakriza, D A Zaky, A K Ilahi, Jean-Philippe Métaxian, R P Nugroho, Sri Widiyantoro, and M F R Auly

Subjects
Data processing, Early results, Receiver function, Time domain, Algorithm, Geology
Abstract

The receiver function method is a method to image the earth subsurface by utilizing Ps conversion waves that are formed due to the velocity boundary. In general, the receiver function method estimates depth of structures such as the mantle-crust boundary by deconvoluting the vertical component from the horizontal component. Typical receiver function data processing is done in the frequency domain where the deconvolution process can be seen as a division between two components. In this study, we tried to reprocess the data using a deconvolution technique in time domain, popularly known as iterative time-domain deconvolution. The principle of iterative time domain deconvolution consists of iterative cross-correlation between the horizontal and vertical component. We use data from the DOMERAPI seismic station network located in the vicinity of Mt Merapi and Mt Merbabu. Mt Merapi is one of the most active volcanoes in the world with frequent eruptions and located at the ring of fire chain volcano in Indonesia. Note that the previous receiver function study in this region showed complex signals at some stations that may be related to sediment at shallow sediment and possible layers of low velocity zone that interfering main signal for a crust-mantle boundary. Our current results show iterative time domain RFs have clearer and smoother signal than the frequency domain that help interpreting the waveform signals. We estimate a range of crust thickness between 26-31 km near Mt Merapi. However, we noticed that iterative time domain calculation requires longer computation time and input signal.

Published
2021
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11. Short term precursors of Strombolian explosions at Yasur volcano (Vanuatu)

Author

Jean Battaglia, Esline Garaebiti, and Jean-Philippe Métaxian

Subjects
geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Geophysics, Induced seismicity, 010502 geochemistry & geophysics, 01 natural sciences, Strombolian eruption, Term (time), Volcano, Impact crater, 13. Climate action, Long period, Gas slug, General Earth and Planetary Sciences, Volcano seismology, Seismology, Geology, 0105 earth and related environmental sciences
Abstract

The seismic wavefield associated with Strombolian activity is usually dominated by explosion quakes (EQs), tremor, and various signals generated by surface phenomena. Looking at the seismicity recorded at Yasur volcano in 2008, we found that beside the transient events which occur simultaneously with surface explosions, the seismicity includes events related to a deeper process. These long period (LP) events form a family of similar events located below the southeastern part of the crater rim at a depth of about 700–1200 m below the summit. They are commonly followed by EQs with a variable delay. The examination of about 20,000 LP-EQ sequences at several stations near the summit shows that interevent delays follow distributions peaked around 11–12 s. This short delay compared to the relatively great source depth of the LPs favors a causal relationship linked to pressure transfer rather than gas slug propagation after nucleation at the LP source.

Published
2016
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12. Migration of seismic activity associated with phreatic eruption at Merapi volcano, Indonesia

Author

Jean-Philippe Métaxian, Muchammad Husni Rizal, Ahmad Ali Fahmi, François Beauducel, I.G. Made Agung Nandaka, Claire Labonne, Agus Budi Santoso, Natalia Poiata, Vadim Monteiller, Corentin Caudron, Noer Cholik, Institut de Physique du Globe de Paris (IPGP (UMR_7154)), Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), and Aix Marseille Université (AMU)

Subjects
010504 meteorology & atmospheric sciences, 010502 geochemistry & geophysics, 01 natural sciences, Dome (geology), Geochemistry and Petrology, [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, Slowness, Volcano, Seismology, Sea level, Phreatic, 0105 earth and related environmental sciences, geography, geography.geographical_feature_category, Array, Merapi, Ray tracing, Phreatic eruption, Ray tracing (physics), Tectonics, Geophysics, [SDU]Sciences of the Universe [physics], Geology
Abstract

International audience; Phreatic activity of Merapi started after nearly 2 years of quiescence following the October-November 2010 eruption which was the largest in more than 100 years. A dozen eruptions identified by visual and/or seismic observations took place between August 2012 and April 2014. We present in this work the results of a detailed analysis of the April 20, 2014 phreatic eruption. We attempted to reconstruct the eruptive process, which lasted for over 30 min. To this end, we determined the wavefield composition by polarization analysis, located high-frequency earthquakes occurring in the initial part of the eruption process and then determined the seismic source migration of low-frequency part of the tremor-like signal [0.3-3 Hz] over time. Source depth of low-frequency signal was obtained by comparing the slowness vector calculated using 3 stations of the seismic antenna with a slowness vector model obtained by ray tracing in the structure, taking into account the topography and a 1D velocity model obtained by spatial auto-correlation analysis. The results allow to distinguish 3 different phases: 1) High-frequency transients interpreted as the result of a sudden decompression caused by the transition of the volcanic fluid to a gaseous phase that occurred approximately 1.5 km deep. This decompression process in the hydrothermal system generated a migration of the low-frequency seismic source from 900 m to 1800 m above sea level; 2) A second decompression process revealed by high-frequency micro-seismicity and associated to the migration of the low-frequency tremor source which is marked first by a descent phase, followed by a sharp ascent until reaching the surface. The evolution of the back-azimuth during the migration process indicates a slight inclination of the conduit, presumably in the orientation of the dome fracture, in the NW-SE direction. This direction is consistent with the alignment of regional tectonic structures and with the directivity of eruption deposits. 3) The seismic source then remains positioned at the altitude of the dome for over 10 min. This phase probably corresponds to the ash emission process. The average migration speed of the low-frequency seismic source from the starting eruptive process to ash emission is about 5 m/s.

Published
2020
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13. Seismic imaging and petrology explain highly explosive eruptions of Merapi Volcano, Indonesia

Author

Antoine Laurin, Ahmad Ali Fahmi, Saskia Erdmann, Phil R. Cummins, Caroline Martel, Andri Dian Nugraha, Sri Widiyantoro, Agus Budi-Santoso, Mohamad Ramdhan, Jean-Philippe Métaxian, Global Geophysics Research Group, Faculty of Mining and Petroleum Engineering, Bandung Institute of Technology, University of Indonesia, Institut des Sciences de la Terre (ISTerre), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Research School of Earth Sciences [Canberra] (RSES), Australian National University (ANU), Institut des Sciences de la Terre d'Orléans - UMR7327 (ISTO), Centre National de la Recherche Scientifique (CNRS)-Université d'Orléans (UO)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), Magma - UMR7327, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)-Centre National de la Recherche Scientifique (CNRS)-Université d'Orléans (UO)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Center for Volcanology and Geological Hazard Mitigation, data were acquired using instruments belonging to the French national pool of portable seismic stations RESIF-SISMOB (CNRS-INSU)., ANR-12-BS06-0012,DOMERAPI,Dynamique d'un volcan d'arc à dômes de lave, le Merapi (Indonésie) : du réservoir magmatique aux processus éruptifs(2012), Institut Teknologi Bandung (ITB), Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)-Observatoire des Sciences de l'Univers en région Centre (OSUC)

Subjects
Moho Depth, 010504 meteorology & atmospheric sciences, Geophysical imaging, Science, 010502 geochemistry & geophysics, 01 natural sciences, Arrival time, Article, Mantle (geology), Petrology, Sea level, 0105 earth and related environmental sciences, geography, Magma Reservoir, Multidisciplinary, geography.geographical_feature_category, Explosive eruption, Magma Plumbing System, Merapi, Hazard potential, Merapi Volcano, Volcano, [SDU]Sciences of the Universe [physics], 13. Climate action, Seismic tomography, Medicine, Geology
Abstract

Our seismic tomographic images characterize, for the first time, spatial and volumetric details of the subvertical magma plumbing system of Merapi Volcano. We present P- and S-wave arrival time data, which were collected in a dense seismic network, known as DOMERAPI, installed around the volcano for 18 months. The P- and S-wave arrival time data with similar path coverage reveal a high Vp/Vs structure extending from a depth of ≥20 km below mean sea level (MSL) up to the summit of the volcano. Combined with results of petrological studies, our seismic tomography data allow us to propose: (1) the existence of a shallow zone of intense fluid percolation, directly below the summit of the volcano; (2) a main, pre-eruptive magma reservoir at ≥ 10 to 20 km below MSL that is orders of magnitude larger than erupted magma volumes; (3) a deep magma reservoir at MOHO depth which supplies the main reservoir; and (4) an extensive, subvertical fluid-magma-transfer zone from the mantle to the surface. Such high-resolution spatial constraints on the volcano plumbing system as shown are an important advance in our ability to forecast and to mitigate the hazard potential of Merapi’s future eruptions.

Published
2018
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14. Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives

Author

Jean-Philippe Métaxian, Orlando Macedo, Jerome Mars, Adolfo Inza, Marielle Malfante, Mauro Dalla Mura, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Géophysique des volcans & géothermie, Institut des Sciences de la Terre (ISTerre), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Instituto Geofisico del Peru (IPG), Instituto Geofísico del Perú (IGP), Universidad Nacional de San Agustín (UNSA), DGA - MRIS, ANR10 LABX56, and ANR-10-LABX-0056,OSUG@2020,Innovative strategies for observing and modelling natural systems(2010)

Subjects
Volcanic hazards, Geospatial analysis, Support Vector Machine, 010504 meteorology & atmospheric sciences, Computer science, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], Big data, 010502 geochemistry & geophysics, computer.software_genre, 01 natural sciences, Machine Learning, Natural hazard, Environmental monitoring, [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, Data Mining, Electrical and Electronic Engineering, 0105 earth and related environmental sciences, Random Forest, business.industry, Applied Mathematics, Scale (chemistry), Automatic Statistical Classification, Feature Space, Signal Representation, Data science, Volcano-seismic signals, 13. Climate action, Index Terms, Signal Processing, Task analysis, business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, LEAPS
Abstract

International audience; Environmental monitoring is a topic of increasing interest, especially concerning the matter of natural hazards prediction. Regarding volcanic unrest, effective methodologies along with innovative and operational tools are needed to monitor, mitigate and prevent risks related to volcanic hazards. In general, the current approaches for volcanoes monitoring are mainly based on the manual analysis of various parameters, including gas leaps, deformations measurements and seismic signals analysis. However, due to the large amount of data acquired by in situ sensors for long term monitoring, manual inspection is no longer a viable option. As in many Big Data situations, classic Machine Learning approaches are now considered to automatize the analysis of years of recorded signals, thereby enabling monitoring at a larger scale. This paper focuses on integrated and operational tools dedicated to the automatic analysis of volcano-seismic signals. Namely we review (i) tools for the optimal representation of volcano-seismic signals (feature space) and the available methods for volcano-seismic events (ii) detection and (iii) classification. We then propose an architecture for the automatic classification of volcano-seismic events. Our prediction system is tested on 6 years of recordings containing 109434 volcano-seismic events acquired from Ubinas volcano (the most active volcano in PerúPer´Perú). Our new proposed model is build using supervised machine learning algorithms (Support Vector Machine) and reaches 92.2% of correct classification over six classes. This prediction model is then used to fully analyze the 6 years of recorded signals.

Published
2018
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15. Machine Learning for Automatic Classification of Volcano-Seismic Signatures

Author

Jean-Philippe Métaxian, Mauro Dalla Mura, Marielle Malfante, Jerome Mars, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut des Sciences de la Terre (ISTerre), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), ANR10 LABX56, DGA/MRIS, and Mars, Jerome

Subjects
Computer science, business.industry, SIGNAL (programming language), Feature extraction, 020206 networking & telecommunications, Linear classifier, 02 engineering and technology, Machine learning, computer.software_genre, Task (project management), Support vector machine, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Transient (computer programming), Data mining, Artificial intelligence, Representation (mathematics), business, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; —The evaluation and prediction of volcanoes activities and associated risks is still a timely and open issue. The amount of volcano-seismic data acquired by recent monitoring stations is huge (e.g., several years of continuous recordings), thereby making machine learning absolutely necessary for their automatic analysis. The transient nature of the volcano-seismic signatures of interest further enforces the need of automatic detection and classification of such events. In this paper, we present a novel architecture for automatic classification of volcano-seismic events based on a comprehensive signal representation with a large feature set. To the best of our knowledge this is one of the first attempts to automatize the classification task of these signals. The proposed approach relies on supervised machine learning techniques to build a prediction model.

Published
2018
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16. Preliminary study results of crustal structure beneath Mount Merapi, Central Java, Indonesia

Author

Sri Widiyantoro, S K Suhardja, Mohamad Ramdhan, and Jean-Philippe Métaxian

Subjects
geography, Discontinuity (geotechnical engineering), geography.geographical_feature_category, Java, Volcano, Receiver function, Low-velocity zone, computer, Seismogram, Geology, Mount, Seismology, computer.programming_language
Abstract

In this study, we put an effort to estimate crustal depth and image crustal structure beneath Merapi volcano by employing multicomponent analysis popularly known as Receiver Function technique. We collected a series of waveforms from teleseismic events recorded from October 2013 to mid-April 2015 at 53 stations as a part of DOMERAPI project. We processed selected seismograms by simple deconvolution process between radial and vertical components to estimate the depth of Moho discontinuity beneath the volcano. Current results show complex structure beneath the volcano and a relatively potential Moho depth at about 30 km, which becomes shallower to the North at about 23 km. Stations located at Southern and Northern area show potential low velocity zone though a velocity modelling is necessary to confirm its depth and how low the velocity is.

Published
2018
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17. Automatic Classification of Volcano Seismic Signatures

Author

Jean-Philippe Métaxian, Adolfo Inza, Jerome Mars, Mauro Dalla Mura, Marielle Malfante, Orlando Macedo, GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Grenoble Images Parole Signal Automatique (GIPSA-lab ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut des Sciences de la Terre (ISTerre), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Faculdad de Geologia, Geofisica y Minas, Universidad Nacional de San Agustin de Arequipa, Universidad Nacional de San Agustín (UNSA), Insituto Geofisico del Peru (IPG), OSUG2020-ANR10 LABX56, ANR-15-IDEX-02, DGA-MRIS, and Instituto Geofísico del Perú (IGP)

Subjects
010504 meteorology & atmospheric sciences, Feature vector, automatic classification, 010502 geochemistry & geophysics, 01 natural sciences, Geochemistry and Petrology, [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, Earth and Planetary Sciences (miscellaneous), Extensive data, Preprocessor, 0105 earth and related environmental sciences, geography, geography.geographical_feature_category, volcano monitoring, Ubinas Volcano, volcano seismic signal, Random forest, Support vector machine, Tectonics, machine learning, Geophysics, Volcano, volcanic hazards, 13. Climate action, Space and Planetary Science, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Classifier (UML), Seismology, Geology
Abstract

International audience; The prediction of volcanic eruptions and the evaluation of associated risks remain a timely and unresolved issue. This paper presents a method to automatically classify seismic events linked to volcanic activity. As increased seismic activity is an indicator of volcanic unrest, automatic classification of volcano seismic events is of major interest for volcano monitoring. The proposed architecture is based on supervised classification, whereby a prediction model is built from an extensive data set of labeled observations. Relevant events should then be detected. Three steps are involved in the building of the prediction model: (i) signals preprocessing, (ii) representation of the signals in the feature space, and (iii) use of an automatic classifier to train the model. Our main contribution lies in the feature space where the seismic observations are represented by 102 features gathered from both acoustic and seismic fields. Ideally, observations are separable in the feature space, depending on their class. The architecture is tested on 109,609 seismic events that were recorded between June 2006 and September 2011 at Ubinas Volcano, Peru. Six main classes of signals are considered: long-period events, volcanic tremors, volcano tectonic events, explosions, hybrid events, and tornillos. Our model reaches 93.5% ± 0.50% accuracy, thereby validating the presented architecture and the features used. Furthermore, we illustrate the limited influence of the learning algorithm used (i.e., random forest and support vector machines) by showing that the results remain accurate regardless of the algorithm selected for the training stage. The model is then used to analyze 6 years of data.

Published
2018
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18. Understanding volcanoes in the Vanuatu arc

Author

Jean-Philippe Métaxian, Jean Battaglia, Esline Garaebiti, Laboratoire Magmas et Volcans (LMV), Institut national des sciences de l'Univers (INSU - CNRS)-Université Jean Monnet [Saint-Étienne] (UJM)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Observatoire de Physique du Globe de Clermont-Ferrand (OPGC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Géophysique des volcans & géothermie, Institut des Sciences de la Terre (ISTerre), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Vanuatu Meteorological and Gehazards Department, Vergniolle, S. (ed.), Métrich, N. (ed.), Observatoire de Physique du Globe de Clermont-Ferrand (OPGC), and Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, Explosion quakes, 010502 geochemistry & geophysics, 01 natural sciences, Strombolian activity, Electrical conduit, Vanuatu, Isotopes, Geochemistry and Petrology, Seismic velocity, Long period, [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, 0105 earth and related environmental sciences, geography, geography.geographical_feature_category, Subduction, Oscillation, Volcano seismology, Strombolian eruption, Continuous data, Mantle source, Similar events, Geophysics, Geochemistry, Volcano, 13. Climate action, Gaua, Yasur, Seismology, Geology
Abstract

We examined one year of seismic recordings collected in 2008 during a temporary experiment at Yasur volcano (Vanuatu). The volcano has a permanent Strombolian activity that was at a relatively high level during most of our experiment with commonly at least one explosion per minute. Associated with this activity, the network recorded intense seismicicity with hundreds of transients per day. Video recordings indicate that most of the high frequency transients are directly related to the strombolian explosions. They also outline the presence of fewer signals which are not accompanied by any surface activity. The classification of transient events recorded at a station close to the summit indicates that a significant part of the events exhibit waveform similarity. This technique allows the identification of characteristic repeating events among the hundreds of thousands of transients recorded during the experiment. Most of the families of similar events are groups of explosion quakes (EQs) but a few are groups of Long Period events related to deeper processes. By scanning the 9 months of continuous data available at the summit station with master events extracted from these families we reconstruct their temporal evolution. Our results show that several families dominate the activity with a few of them lasting for several months. We show that their temporal evolutions can be used to probe changes in the structure or activity of the volcano. We observe that a major change was induced by a M = 7.3 subduction earthquake which occurred on April 9, 2008 about 80 km from the volcano. While this event did not change significantly the surface morphology of the volcano nor the intensity of the eruptive activity, it interrupted the families as none of them is present both before and after the event. This change in the waveforms can be explained by a drop in the seismic velocity of the volcano caused by the distal event. Numerous other transitions between families are observed, sudden or progressive. These can be interpreted as representative of changes in the eruptive dynamics. The presence of similar EQs, especially for impulsive explosions, indicates that the source mechanism is reproducible and has a stable location for some periods. This favors a source process based on the oscillation of the conduit or oscillation of the edifice in response to the explosive decompression of gas slugs at the free surface of the conduit. Our results suggest that the seismic activity of Yasur is characterized by the presence of dominant modes of resonance of the conduit which may be influenced both by external and internal factors.

Published
2016
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19. Seismo-volcano source localization with triaxial broad-band seismic array

Author

Jean-Philippe Métaxian, Orlando Macedo, Gareth S. O'Brien, Jerome Mars, and L. A. Inza

Subjects
Seismometer, geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, 010502 geochemistry & geophysics, 01 natural sciences, Seismic wave, Geophysics, Volcano, Impact crater, 13. Climate action, Geochemistry and Petrology, Seismic array, Epicenter, Antenna (radio), Digital elevation model, Geology, Seismology, 0105 earth and related environmental sciences
Abstract

SUMMARY Seismo-volcano source localization is essential to improve our understanding of eruptive dynamics and of magmatic systems. The lack of clear seismic wave phases prohibits the use of classical location methods. Seismic antennas composed of one-component (1C) seismometers provide a good estimate of the backazimuth of the wavefield. The depth estimation, on the other hand, is difficult or impossible to determine. As in classical seismology, the use of three-component (3C) seismometers is now common in volcano studies. To determine the source location parameters (backazimuth and depth), we extend the 1C seismic antenna approach to 3Cs. This paper discusses a high-resolution location method using a 3C array survey (3C-MUSIC algorithm) with data from two seismic antennas installed on an andesitic volcano in Peru (Ubinas volcano). One of the main scientific questions related to the eruptive process of Ubinas volcano is the relationship between the magmatic explosions and long-period (LP) swarms. After introducing the 3C array theory, we evaluate the robustness of the location method on a full wavefield 3-D synthetic data set generated using a digital elevation model of Ubinas volcano and an homogeneous velocity model. Results show that the backazimuth determined using the 3C array has a smaller error than a 1C array. Only the 3C method allows the recovery of the source depths. Finally, we applied the 3C approach to two seismic events recorded in 2009. Crossing the estimated backazimuth and incidence angles, we find sources located 1000 ± 660 m and 3000 ± 730 m below the bottom of the active crater for the explosion and the LP event, respectively. Therefore, extending 1C arrays to 3C arrays in volcano monitoring allows a more accurate determination of the source epicentre and now an estimate for the depth.

Published
2011
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20. Low-frequency bursts of horizontally polarized waves in the Arctic sea-ice cover

Author

Jari Haapala, Jacques Grangeon, David Marsan, Jean-Philippe Métaxian, Pierre-Francois Roux, Jérôme Weiss, Laboratoire de Géophysique Interne et Tectonophysique (LGIT), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Central des Ponts et Chaussées (LCPC)-Centre National de la Recherche Scientifique (CNRS), EDGe, Laboratoire de glaciologie et géophysique de l'environnement (LGGE), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Finnish Institute of Marine Research (FIMR), European Project, Mécanique des failles, Institut des Sciences de la Terre (ISTerre), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut national des sciences de l'Univers (INSU - CNRS)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université Joseph Fourier - Grenoble 1 (UJF)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut national des sciences de l'Univers (INSU - CNRS)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université Joseph Fourier - Grenoble 1 (UJF), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS), Géophysique des volcans, Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut national des sciences de l'Univers (INSU - CNRS)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université Joseph Fourier - Grenoble 1 (UJF), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)

Subjects
Shearing (physics), Seismometer, geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Geophysics, Low frequency, 01 natural sciences, Swell, Physics::Geophysics, Amplitude, Arctic, Broadband, Sea ice, [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology, Seismology, Geology, 0105 earth and related environmental sciences, Earth-Surface Processes
Abstract

We report the detection of bursts of low-frequency waves, typicallyf= 0.025 Hz, on horizontal channels of broadband seismometers deployed on the Arctic sea-ice cover during the DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies) experiment in spring 2007. These bursts have amplitudes well above the ambient ice swell and a lower frequency content. Their typical duration is of the order of minutes. They occur at irregular times, with periods of relative quietness alternating with periods of strong activity. A significant correlation between the rate of burst occurrences and the ice-cover deformation at the ∼400 km scale centered on the seismic network suggests that these bursts are caused by remote, episodic deformation involving shearing across regional-scale leads. This observation opens the possibility of complementing satellite measurements of ice-cover deformation, by providing a much more precise temporal sampling, hence a better characterization of the processes involved during these deformation events.

Published
2011
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21. Time reverse location of seismic long-period events recorded on Mt Etna

Author

Ivan Lokmer, L. De Barros, Gareth S. O'Brien, Domenico Patanè, Jean-Philippe Métaxian, Gilberto Saccorotti, and Christopher J. Bean

Subjects
geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Wave propagation, Energy current, Inverse transform sampling, Inversion (meteorology), 010502 geochemistry & geophysics, Geodesy, 01 natural sciences, Seismic wave, Geophysics, Volcano, Geochemistry and Petrology, Long period, Hyperparameter optimization, Seismology, Geology, 0105 earth and related environmental sciences
Abstract

We present the first application of a time reverse location method in a volcanic setting, for a family of long-period (LP) events recorded on Mt Etna. Results are compared with locations determined using a full moment tensor grid search inversion and cross-correlation method. From 2008 June 18 to July 3, 50 broad-band seismic stations were deployed on Mt Etna, Italy, in close proximity to the summit. Two families of LP events were detected with dominant spectral peaks around 0.9 Hz. The large number of stations close to the summit allowed us to locate all events in both families using a time reversal location method. The method involves taking the seismic signal, reversing it in time, and using it as a seismic source in a numerical seismic wave simulator where the reversed signals propagate through the numerical model, interfere constructively and destructively, and focus on the original source location. The source location is the computational cell with the largest displacement magnitude at the time of maximum energy current density inside the grid. Before we located the two LP families we first applied the method to two synthetic data sets and found a good fit between the time reverse location and true synthetic location for a known velocity model. The time reverse location results of the two families show a shallow seismic region close to the summit in agreement with the locations using a moment tensor full waveform inversion method and a cross-correlation location method.

Published
2010
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22. Complex behavior and source model of the tremor at Arenal volcano, Costa Rica

Author

Philippe Lesage, Mauricio M. Mora, Jean-Philippe Métaxian, Guillermo E. Alvarado, Javier F. Pacheco, Laboratoire de Géophysique Interne et Tectonophysique (LGIT), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Laboratoire Central des Ponts et Chaussées (LCPC)-Institut des Sciences de la Terre (ISTerre), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut national des sciences de l'Univers (INSU - CNRS)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), Instituto de Geografia (LAFQA), National Autonomous University of Mexico (UNAM), Escuela Centroamericana de Geología, Universidad Nacional de Costa Rica, Observatorio Sismológico y Vulcanológico de Arenal y Miravalles, Instituto Costarricense de Electricidad, and Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Central des Ponts et Chaussées (LCPC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
source model, 010504 meteorology & atmospheric sciences, Oscillation, volcano seismology, Harmonic tremor, Musical instrument, volcanic tremor, Acoustic wave, Geophysics, explosion quake, 010502 geochemistry & geophysics, 01 natural sciences, Seismic wave, Resonator, Impact crater, 13. Climate action, Geochemistry and Petrology, Magma, Arenal volcano, Geology, Seismology, 0105 earth and related environmental sciences
Abstract

International audience; Typical records of volcanic tremor and explosion quakes at Arenal volcano are analyzed with a high-resolution time-frequency method. The main characteristics of these seismic signals are: (1) numerous regularly spaced spectral peaks including both odd and even overtones; (2) frequency gliding in the range [0.9–2] Hz of the fundamental peak; (3) frequency jumps with either positive or negative increments; (4) tremor episodes with two simultaneous systems of spectral peaks affected by independent frequency gliding; (5) progressive transitions between spasmodic tremor and harmonic tremor; (6) lack of clear and systematic relationship between the occurrence of explosions and tremor. Some examples of alternation between two states of oscillation characterized by different fundamental frequencies are also observed. Some tremor and explosion codas are characterized by acoustic and seismic waves with identical spectral content and frequency gliding, which suggests a common excitation process. We propose a source model for the tremor at Arenal in which intermittent gas flow through fractures produces repetitive pressure pulses. The repeating period of the pulses is stabilized by a feedback mechanism associated with standing or traveling waves in the magmatic conduit. The pressure pulses generate acoustic waves in the atmosphere and act as excitation of the interface waves in the conduit. When the repeating period of the pulses is stable enough, they produce regularly spaced spectral peaks by the Dirac comb effect and hence harmonic tremor. When the period stability is lost, because of failures in the feedback mechanism, the tremor becomes spasmodic. The proposed source model of tremor is similar to the sound emission process of a clarinet. Fractures in the solid or viscous layer capping the lava pool in the crater act as the clarinet reed, and the conduit filled with low velocity bubbly magma is equivalent to the pipe of the musical instrument. The frequency gliding is related to variations of the pressure in the conduit, which modify the gas fraction, the wave velocity and, possibly, the length of the resonator. Moreover, several observations suggest that two seismic sources, associated with two magmatic conduits, are active in Arenal volcano. They could explain in particular the apparent independence of tremor and explosions and the episodes of tremor displaying two simultaneous systems of spectral peaks.

Published
2006
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23. Earthquake location determination using data from DOMERAPI and BMKG seismic networks: A preliminary result of DOMERAPI project

Author

Sri Widiyantoro, Ayunda Aulia Valencia, Jean-Philippe Métaxian, Andri Dian Nugraha, and Mohamad Ramdhan

Subjects
Earthquake scenario, Seismometer, geography, Volcanic hazards, geography.geographical_feature_category, Volcano, Hypocenter, Magma, Urban seismic risk, Geology, Seismology, Earthquake location
Abstract

DOMERAPI project has been conducted to comprehensively study the internal structure of Merapi volcano, especially about deep structural features beneath the volcano. DOMERAPI earthquake monitoring network consists of 46 broad-band seismometers installed around the Merapi volcano. Earthquake hypocenter determination is a very important step for further studies, such as hypocenter relocation and seismic tomographic imaging. Ray paths from earthquake events occurring outside the Merapi region can be utilized to delineate the deep magma structure. Earthquakes occurring outside the DOMERAPI seismic network will produce an azimuthal gap greater than 1800. Owing to this situation the stations from BMKG seismic network can be used jointly to minimize the azimuthal gap. We identified earthquake events manually and carefully, and then picked arrival times of P and S waves. The data from the DOMERAPI seismic network were combined with the BMKG data catalogue to determine earthquake events outside the Merapi region. For future work, we will also use the BPPTKG (Center for Research and Development of Geological Disaster Technology) data catalogue in order to study shallow structures beneath the Merapi volcano. The application of all data catalogues will provide good information as input for further advanced studies and volcano hazards mitigation.

Published
2015
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24. Seismic Travel-time Tomography beneath Merapi Volcano and its Surroundings: A Preliminary Result from DOMERAPI Project

Author

Jean-Philippe Métaxian, Agus Budi Santoso, Andry Syaly Sembiring, Andri Dian Nugraha, Mohamad Ramdhan, Sri Widiyantoro, Said Kristyawan, and Asep Saepuloh

Subjects
Travel time, geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Volcano, Magma, Tomography, 010502 geochemistry & geophysics, 01 natural sciences, Seismology, Geology, 0105 earth and related environmental sciences
Published
2017
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26. Permanent tremor of Masaya Volcano, Nicaragua: Wave field analysis and source location

Author

Jacques Dorel, Philippe Lesage, Jean-Philippe Métaxian, Institut des Sciences de la Terre (ISTerre), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-Université Grenoble Alpes (UGA), Laboratoire Magmas et Volcans (LMV), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement et la société-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Observatoire de Physique du Globe de Clermont-Ferrand (OPGC), and Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)

Subjects
Atmospheric Science, Lava, Soil Science, Aquatic Science, Oceanography, Seismic wave, symbols.namesake, Impact crater, Geochemistry and Petrology, [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, Earth and Planetary Sciences (miscellaneous), Rayleigh wave, Earth-Surface Processes, Water Science and Technology, geography, geography.geographical_feature_category, Ecology, Paleontology, Forestry, Love wave, Geophysics, Volcano, Space and Planetary Science, Surface wave, Seismic array, symbols, Geology, Seismology
Abstract

International audience; The Masaya Volcano, Nicaragua, is a basaltic caldera in a subduction zone. The permanent source of the volcanic tremor was located inside Santiago crater, at the lava lake's position and 400 m below the NE rim, and therefore corresponds to superficial magma activity. We used two tripartite arrays (90 m side), one semicircular array (r=120 m) in 1992, and two semicircular arrays (r=60 m) and a 2500 m long linear array radiating out from the source and on the flank of the crater in 1993. We used both a cross-spectrum method and a correlation method to determine the wave delay time between the reference station and the other stations of an array and to quantify the wave field. Using the delays therefore by intersecting the back azimuth wave directions from the arrays, we could pinpoint the source. Additionally, the correlation coefficients obtained as functions of frequency for the three components of motion confirm the inferred position of the source of tremor. The tremor's wave field is composed of comparable quantities of dispersed Rayleigh and Love surface waves, whose phase velocities lie in the ranges 730-1240 m/s at 2 Hz and 330-550 m/s at 6 Hz. The dispersive phase velocities were inverted to obtain crustal structures with a minimal number of layers. The resulting velocity models are similar for the northern and southern parts of the volcano. After geometrical spreading corrections, Q2Hz = 14 and Q3Hz =31 were determined along the northern linear array. The typical low velocities and low Q corresponding to the cone structure and are similar to those of other basaltic volcanoes like Puu Oo, Hawaii, and Klyuchevskoy, Kamchatka.

Published
1997
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27. Long-period seismicity in the shallow volcanic edifice formed from slow-rupture earthquakes

Author

Gareth O’ Brien, Christopher J. Bean, Shane Murphy, Louis De Barros, Ivan Lokmer, Jean-Philippe Métaxian, School of Geological Sciences [Dublin], University College Dublin [Dublin] (UCD), Géoazur (GEOAZUR 6526), Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Institut des Sciences de la Terre (ISTerre), Centre National de la Recherche Scientifique (CNRS)-PRES Université de Grenoble-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Tullow Oil plc, Environmental Sciences Research Institute [Coleraine], University of Ulster, Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)

Subjects
geography, geography.geographical_feature_category, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, Geophysics, Induced seismicity, Volcano seismicity, Eruption forecasting, Volcano, 13. Climate action, Long period, General Earth and Planetary Sciences, Volcano seismology, Seismology, Geology
Abstract

Despite recent technological advances in volcano monitoring, eruption forecasting is still inadequate. Improved forecasting requires a deeper understanding of when unrest will lead to an actual eruption. Shallow Long Period (low spectral frequency) seismic events are routinely employed as a primary tool in forecasting strategies as they often precede eruptions. They are universally explained as resonating fluid-filled cracks or conduits, indicating the presence of mechanically active near-surface fluids. We undertake very high resolution seismic field experiments at Mt Etna, Italy; Turrialba, Costa Rica and Ubinas, Peru, in which we find that seismogram resonance is propagation path related whilst the seismic sources comprise short pulses. Data analysis and numerical modelling show that slow-rupture failure in unconsolidated volcanic materials reproduces all key aspects of these new observations. Contrary to current interpretations, here we show that our observed Long Period events are not direct indicators of fluid presence/migration, but rather are markers for upper edifice deformation. This finding encapsulates this seismicity within growing observations of a spectrum of deformation rates in other non-volcanic environments, from slow-slip earthquakes through fast dynamic rupture. It calls for a reassessment of how lowfrequency seismic signals are interpreted in their key role in eruption forecasting. AD 17/02/2014

Published
2013
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28. Analysis of dynamics of vulcanian activity of Ubinas volcano, using multicomponent seismic antennas

Author

L. A. Inza, Orlando Macedo, D. Zandomeneghi, Jerome Mars, Christopher J. Bean, Jean-Philippe Métaxian, Gareth S. O'Brien, Géophysique des volcans & géothermie, Institut des Sciences de la Terre (ISTerre), Centre National de la Recherche Scientifique (CNRS)-PRES Université de Grenoble-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-PRES Université de Grenoble-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), School of Geological Sciences [Dublin], University College Dublin [Dublin] (UCD), Instituto Geofisico del Peru (IPG), Instituto Geofísico del Perú (IGP), and Centre National de la Recherche Scientifique (CNRS)-PRES Université de Grenoble-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])

Subjects
Series (stratigraphy), geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, vulcanian activity, Source estimation, Geophysics, 010502 geochemistry & geophysics, 3Components, 01 natural sciences, tremor, Volcano, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 13. Climate action, Geochemistry and Petrology, Ubinas, Seismic array, Interval (graph theory), Antennas, Source depth Estimation, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Seismology, Geology, 0105 earth and related environmental sciences
Abstract

International audience; A series of 16 vulcanian explosions occurred at Ubinas volcano betweenMay 24 and June 14, 2009. The intervals between explosions were from 2.1 h to more than 6 days (mean interval, 33 h). Considering only the first nine explosions, the average time interval was 7.8 h. Most of the explosions occurred after a short time interval (< 8 h) and had low energy, which suggests that the refilling time was not sufficient for large accumulation of gas. A tremor episode followed 75% of the explosions, which coincided with pulses of ash emission. The durations of the tremors following the explosionswere longer for the two highest energy explosions. To better understand the physical processes associated with these eruptive events, we localized the sources of explosions using two seismic antennas that were composed of three component 10 and 12 sensors. We used the high-resolution MUSIC-3C algorithm to estimate the slowness vector for the first waves that composed the explosion signals recorded by the two antennas assuming propagation in a homogeneous medium. The initial part of the explosions was dominated by two frequencies, at 1.1 Hz and 1.5 Hz, for which we identified two separated sources located at 4810 m and 3890 m +/− 390 altitude, respectively. The position of these two sources was the same for the full 16 explosions. This implies the reproduction of similar mechanisms in the conduit. Based on the eruptive mechanisms proposed for other volcanoes of the same type, we interpret the position of these two sources as the limits of the conduit portion that was involved in the fragmentation process. Seismic data and ground deformation recorded simultaneously less than 2 km from the crater showed a decompression movement 2 s prior to each explosion. This movement can be interpreted as gas leakage at the level of the cap before its destruction. The pressure drop generated in the conduit could be the cause of the fragmentation process that propagated deeper. Based on these observations, we interpret the position of the highest source as the part of the conduit under the cap, and the deeper source as the limit of the fragmentation zone.

Published
2013
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29. Highly Explosive 2010 Merapi Eruption: Evidence for Shallow-Level Crustal Assimilation and Hybrid Fluid

Author

Indyo Pratomo, Philippe de Parseval, Sri Sumarti, Anastassia Y. Borisova, Surono, Sophie Gouy, Jean-Paul Toutain, Jean-Philippe Métaxian, Caroline Martel, Ilya N. Bindeman, Géosciences Environnement Toulouse (GET), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD), Geological Department, Lomonosov Moscow State University (MSU), Institut des Sciences de la Terre d'Orléans - UMR7327 (ISTO), Centre National de la Recherche Scientifique (CNRS)-Université d'Orléans (UO)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), Museum Geologi, Volcano Investigation and Technology Development Institution, Institut de Recherche pour Développement, Geological Sciences, University of Oregon [Eugene], Géophysique des volcans & géothermie, Institut des Sciences de la Terre (ISTerre), Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), CVGHM, Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)-Observatoire des Sciences de l'Univers en région Centre (OSUC), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)

Subjects
decarbonation reaction, bulk and selective assimilation, 010504 meteorology & atmospheric sciences, Subplinian eruption, Merapi volcano, [SDE.MCG]Environmental Sciences/Global Changes, Geochemistry, Silicic, Pyroclastic rock, engineering.material, 010502 geochemistry & geophysics, 01 natural sciences, Basaltic andesite, Geochemistry and Petrology, subplinian eruption, [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, Plagioclase, Xenolith, CO2 mass and pressure, Tephra, 0105 earth and related environmental sciences, magmatic fluid, Ash-leachate, AFC, Geophysics, Augite, 13. Climate action, Indonesia, Magmatic fluid, engineering, Phenocryst, Decarbonation reaction, Bulk and selective assimilation, Geology, ash-leachate
Abstract

The processes responsible for the highly explosive events at Merapi, Central Java, Indonesia have been investigated through a petrological, mineralogical and geochemical study of the first-stage tephra and pyroclastic flows sampled in October and November 2010, and second-stage ash sampled shortly after the 5–6th November 2010 paroxysmal subplinian eruption. Several chemical and physical parameters suggest that the magma assimilated calc-silicate xenoliths derived from the surrounding carbonate-bearing crust (Javanese limestone). The bulk volcanic samples have highly radiogenic 87 Sr/ 86 Sr (0.70571–0.70598) ratios that approach the compositional field of material similar to the calc-silicate xenoliths. The 2010 plagioclase phenocrysts from the pyroclastic flow and tephra reveal anorthite cores (up to An 94–97 ) with low FeO contents (≤ 0.8 wt.%), and 18 O enrichment (6.5‰ δ 18 O). The major and trace elements of the silicic glasses and phenocrysts (plagioclase, low-Al augite and titanomagnetite), the Sr-isotopic compositions of the bulk samples and plagioclases erupted in 2010 can be explained by complete digestion of the 1998 and 2006 calc-silicate xenoliths. The bulk assimilation proceeded through binary mixing between a calcic melt (representing Crustal Assimilant, CaO up to 10.5 wt.% and CaO/Al 2 O 3 up to 1.2) and the deep source hydrous K-rich melt. Similarly to the 1998 and 2006 calc-silicate xenolith composition, the 2010 Crustal Assimilant is enriched in Mn (MnO up to 0.5 wt.%), Zn, V, and Sc contents. In contrast, the hydrous K-rich melt is enriched in volatiles (Cl up to 0.37 wt.% and bulk H 2 O + CO 2 up to 5 ± 1 wt.%), Al 2 O 3 , TiO 2 and REE contents, consistent with its derivation from deep source. This hydrous K-rich melt may have been saturated with an aqueous Cl-rich fluid at about 200 MPa, a pressure consistent with the level of the crustal assimilation. We estimated that the pre-eruptive basaltic andesite magma assimilated from 15 to 40 wt.% of the calc-silicate crustal material, corresponding to introduction of additional 0.19 to 2.1 Mt of CO 2 to the magma. Experimental leaching of the ash samples documents the release of an aqueous fluid enriched in Cl, Na, Ca, Cd, Sb and Zn during the paroxysmal subplinian eruption. The paroxysmal eruption may have been produced by saturation of the pre-eruptive basaltic andesite magma with hybrid aqueous carbonic NaCl–HCl-rich fluid due to bulk assimilation creating elevated partial pressure of CO 2 at shallow crustal conditions of about 200 MPa. In contrast, mildly explosive block-and-ash flows (typical Merapi-type) may result from selective assimilation of the carbonate-bearing xenoliths and lower CO 2 partial pressure that may not lead to explosive degassing.

Published
2013
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30. Analysis of the seismic activity associated with the 2010 eruption of Merapi volcano, Java

Author

Agus Budi-Santoso, Philippe Lesage, Surono, Jean-Philippe Métaxian, Philippe Jousset, Subandriyo, Sri Sumarti, Sapari Dwiyono, Institut des Sciences de la Terre (ISTerre), Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Balai Penyelidikan dan Pengembangan Teknologi Kegunungapian, BPPTK, Géophysique des volcans & géothermie, Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Center of Volcanology and Geological Hazard Mitigation, CVGHM, Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), ANR-12-BS06-0012,DOMERAPI,Dynamique d'un volcan d'arc à dômes de lave, le Merapi (Indonésie) : du réservoir magmatique aux processus éruptifs(2012), and European Project

Subjects
source location, 010504 meteorology & atmospheric sciences, Merapi volcano, [SDE.MCG]Environmental Sciences/Global Changes, volcano seismology, Induced seismicity, 010502 geochemistry & geophysics, 01 natural sciences, RSAM, Eruption forecasting, Effusive eruption, Geochemistry and Petrology, Pre-eruptive seismicity, [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology, 0105 earth and related environmental sciences, Series (stratigraphy), geography, Explosive eruption, geography.geographical_feature_category, Volcano seismology, Merapi Volcano, Phreatic eruption, Source Location, Geophysics, Volcano, 13. Climate action, Magma, eruption forecasting, Geology, Seismology, Occurrence time, Material Failure Forecast Method
Abstract

The 2010 eruption of Merapi is the first large explosive eruption of the volcano that has been instrumentally observed. The main characteristics of the seismic activity during the pre-eruptive period and the crisis are presented and interpreted in this paper. The first seismic precursors were a series of four shallow swarms during the period between 12 and 4 months before the eruption. These swarms are interpreted as the result of perturbations of the hydrothermal system by increasing heat flow. Shorter-term and more continuous precursory seismic activity started about 6 weeks before the initial explosion on 26 October 2010. During this period, the rate of seismicity increased almost constantly yielding a cumulative seismic energy release for volcano-tectonic (VT) and multiphase events (MP) of 7.5 × 10 10 J. This value is 3 times the maximum energy release preceding previous effusive eruptions of Merapi. The high level reached and the accelerated behavior of both the deformation of the summit and the seismic activity are distinct features of the 2010 eruption. The hypocenters of VT events in 2010 occur in two clusters at of 2.5 to 5 km and less than 1.5 km depths below the summit. An aseismic zone was detected at 1.5–2.5 km depth, consistent with studies of previous eruptions, and indicating that this is a robust feature of Merapi's subsurface structure. Our analysis suggests that the aseismic zone is a poorly consolidated layer of altered material within the volcano. Deep VT events occurred mainly before 17 October 2010; subsequent to that time shallow activity strongly increased. The deep seismic activity is interpreted as associated with the enlargement of a narrow conduit by an unusually large volume of rapidly ascending magma. The shallow seismicity is interpreted as recording the final magma ascent and the rupture of a summit-dome plug, which triggered the eruption on 26 October 2010. Hindsight forecasting of the occurrence time of the eruption is performed by applying the Material Failure Forecast Method (FFM) using cumulative Real-time Seismic Amplitude (RSAM) calculated both from raw records and on signals classified according to their dominant frequency. Stable estimates of eruption time with errors as small as ± 4 h are obtained within a 6 day lapse time before the eruption. This approach could therefore be useful to support decision making in the case of future large explosive episodes at Merapi.

Published
2013
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31. Earthquake-volcano interaction imaged by coda wave interferometry

Author

Esline Garaebiti, Jean Battaglia, and Jean-Philippe Métaxian

Subjects
geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, 010502 geochemistry & geophysics, Spatial distribution, 01 natural sciences, Seismic wave, Coda, Interferometry, Geophysics, Amplitude, Volcano, 13. Climate action, Large earthquakes, General Earth and Planetary Sciences, Response Amplitude, Seismology, Geology, 0105 earth and related environmental sciences
Abstract

Large earthquakes are often assumed to influence the eruptive activity of volcanoes. A major challenge to better understand the causal relationship between these phenomena is to detect and image, in detail, all induced changes, including subtle, non-eruptive responses. We show that coda wave interferometry can be used to image such earthquake-induced responses, as recorded at Yasur volcano (Vanuatu) following a magnitude 7.3 earthquake which occurred 80 km from its summit. We use repeating Long-Period events to show that the earthquake caused a sudden seismic velocity drop, followed by a slow partial recovery process. The spatial distribution of the response amplitude indicates an effect centered on the volcano. Our result demonstrates that, even if no major change in eruptive activity is observed, volcanoes will be affected by the propagation of large amplitude seismic waves through their structure, suggesting that Earthquake-volcano interaction is likely a more common phenomenon than previously believed.

Published
2012
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32. Sea-ice thickness measurement based on the dispersion of ice swell

Author

Eric Larose, Jean-Philippe Métaxian, David Marsan, and Jéro^Me Weiss

Subjects
Seismometer, geography, geography.geographical_feature_category, Acoustics and Ultrasonics, Acoustics, Plane wave, Geodesy, Acoustic dispersion, Swell, Physics::Geophysics, Superposition principle, Arts and Humanities (miscellaneous), Sea ice thickness, Sea ice, Dispersion (water waves), Geology
Abstract

The dispersion of flexural waves propagating in the Arctic sea ice cover is exploited in order to locally measure the ice thickness. The observed dispersion, for waves filtered in the 4-20 s period interval, at up to 4 broad-band seismometers deployed in Spring 2007 near the North Pole, is compared to a parameterized model that accounts for a complex wavefield made of a superposition of independent plane waves with different amplitudes and back-azimuth angles. The parameterization, that includes finding the best modeled ice thickness, is performed by using the cross-correlation functions between the seismometers. The ice thickness is estimated to 2.5+/-0.2 m for the similar to 1 km-large floe the seismic stations were deployed on, which is coherent with other, independent measurements at this site. This study thus demonstrates the feasibility of using broad-band seismometers deployed on the sea-ice in order to passively measure the ice thickness, without requiring active sources nor human intervention.

Published
2012

33. Estimation of the near-surface velocity structure of the Yasur-Yenkahe volcanic complex, Vanuatu

Author

Laurence Perrier, Esline Garaebiti, Jean Battaglia, Jean-Philippe Métaxian, Institut des Sciences de la Terre (ISTerre), Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Géophysique des volcans & géothermie, Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Laboratoire Magmas et Volcans (LMV), Observatoire de Physique du Globe de Clermont-Ferrand (OPGC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), Vanuatu Meteorological and Gehazards Department, Institut national des sciences de l'Univers (INSU - CNRS)-Université Jean Monnet [Saint-Étienne] (UJM)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Observatoire de Physique du Globe de Clermont-Ferrand (OPGC), and Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], [SDE.MCG]Environmental Sciences/Global Changes, Inversion (geology), Pyroclastic rock, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], Velocity structure, Seismic noise, 010502 geochemistry & geophysics, 01 natural sciences, Hydrothermal circulation, symbols.namesake, SPAC method, Geochemistry and Petrology, Caldera, Rayleigh scattering, Dispersion (water waves), 0105 earth and related environmental sciences, Yasur volcano, geography, geography.geographical_feature_category, Volcano seismology, Geophysics, Volcano, 13. Climate action, symbols, f-k method, Seismology, Geology
Abstract

International audience; Small-aperture array measurements of seismic noise at seven sites around Yasur volcano (Vanuatu) are performed to estimate the VP and VS velocities of the shallow structure. The spatial autocorrelation (SPAC) and the frequency-wavenumber (f-k) methods are used to determine Rayleigh phase velocity dispersion curves. Phase velocities computed with the SPAC method vary between 580 m/s and 960 m/s at 1 Hz and between 270 m/s and 420 m/s at 15 Hz. F-k dispersion curves show velocities of 300-340 m/s and 800-940 m/s at 1 Hz and 200-230 m/s at 15 Hz. An inversion technique based on the use of the neighbourhood algorithm has been used to calculate the one-dimensional velocity model at each site. Velocity models reach 200 m deep and mainly contain two layers and a half-space. For sites close to the Siwi caldera rims, comparisons with geology and hydrothermal system studies suggest that the two layers highlighted in models may correspond to two large pyroclastic sequences related to caldera collapses based on the flank of an old volcano. Results obtained for the other three sites, located inside the caldera, show the influence of the hydrothermal system on P- and S-wave velocities. For these sites, fluid circulation inside the volcanic deposits causes lower velocities at depth. To obtain a near-surface velocity model of the volcanic structure, each 1D velocity model is spatially extrapolated according to the surface geology. Results highlight four distinct areas, the Siwi caldera edges with high velocities and the resurgent block, the ash plain and the Yasur edifice with lower velocities at depth.

Published
2012
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34. Localization with multicomponent seismic array

Author

Jean-Philippe Métaxian, L.A. Inza, Orlando Macedo, Jerome Mars, Gareth S. O'Brien, Institut des Sciences de la Terre (ISTerre), Centre National de la Recherche Scientifique (CNRS)-PRES Université de Grenoble-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), Département Images et Signal (GIPSA-DIS), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), School of Geological Sciences, University College Dublin, University College Dublin [Dublin] (UCD), Instituto Geofisico del Peru (IPG), Instituto Geofísico del Perú (IGP), ISTERRE Chambery, Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)

Subjects
Seismometer, geography, geography.geographical_feature_category, 3C-MUSIC algorithm, 010504 meteorology & atmospheric sciences, 020206 networking & telecommunications, 02 engineering and technology, 01 natural sciences, Seismic wave, Seismo-volcano source localization, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Volcano, Robustness (computer science), Epicenter, Seismic array, 0202 electrical engineering, electronic engineering, information engineering, Antenna (radio), Digital elevation model, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Geology, Seismology, 0105 earth and related environmental sciences, Remote sensing
Abstract

International audience; Seismo-volcano source localization is essential to improve our understanding of volcano systems. The lack of clear seismic wave phases prohibits the use of classical location methods. Seismic antennas composed of one-component (1C) seismometers provide a good estimate of the back-azimuth of the wavefield. The depth estimation, on the other hand, is difficult or impossible to determine. In order to determine the source location parameters (back-azimuth and depth), we extend the 1C seismic antenna approach to 3Cs. This communication discusses a high resolution location method using a 3C array survey (3C-MUSIC algorithm) with data from two seismic antennas installed on an andesitic volcano in Peru (Ubinas volcano). After introducing the 3C MUSIC processing, we evaluate the robustness of the location method on a full wavefield 3D synthetic dataset generated using a digital elevation model of Ubinas volcano and an homogeneous velocity model. Results show that the back-azimuth determined using the 3C array has a smaller error than a 1C array. Only the 3C method allows the recovery of the source depths. Finally, we applied the 3C-MUSIC to two seismic events recorded in 2009. Therefore, extending 1C arrays to 3C arrays in volcano monitoring allows a more accurate determination of the source epicenter and now an estimate for the depth.

Published
2011
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35. Short term forecasting of explosions at Ubinas volcano, Perú

Author

Adolfo Inza, Paola Traversa, Jean-Robert Grasso, Edu Taipe, Jean-Philippe Métaxian, Orlando Macedo, and Olivier Lengliné

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, Soil Science, Aquatic Science, Seismic noise, Induced seismicity, 010502 geochemistry & geophysics, Oceanography, 01 natural sciences, Acceleration, Geochemistry and Petrology, Long period, Earth and Planetary Sciences (miscellaneous), 0105 earth and related environmental sciences, Earth-Surface Processes, Water Science and Technology, geography, geography.geographical_feature_category, Ecology, Paleontology, Forestry, Term (time), Geophysics, Volcano, 13. Climate action, Space and Planetary Science, Magma, Pressure increase, Seismology, Geology
Abstract

Most seismic eruption forerunners are described using Volcano-Tectonic earthquakes, seismic energy release, deformation rates or seismic noise analyses. Using the seismic data recorded at Ubinas volcano (Peru) between 2006 and 2008, we explore the time evolution of the Long Period (LP) seismicity rate prior to 143 explosions. We resolve an average acceleration of the LP rate above the background level during the 2-3 hours preceding the explosion onset. Such an average pattern, which emerges when stacking over LP time series, is robust and stable over all the 2006-2008 period, for which data is available. This accelerating pattern is also recovered when conditioning the LP rate on the occurrence of an other LP event, rather than on the explosion time. It supports a common mechanism for the generation of explosions and LP events, the magma conduit pressure increase being the most probable candidate. The average LP rate acceleration toward an explosion is highly significant prior to the higher energy explosions, supposedly the ones associated with the larger pressure increases. The dramatic decay of the LP activity following explosions, still reinforce the strong relationship between these two processes. We test and we quantify the retrospective forecasting power of these LP rate patterns to predict Ubinas explosions. The prediction quality of the forecasts (e.g. for 17% of alarm time, we predict 63% of Ubinas explosions, with 58% of false alarms) is evaluated using error diagrams. The prediction results are stable and the prediction algorithm validated, i.e. its performance is better than the random guess.

Published
2011
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36. First Petrologic and Geochemical Data on the 2010 Merapi Eruption (Indonesia)

Author

Anastassia Yu Borisova, Caroline MARTEL, Indyo Pratomo, Jean-Paul Toutain, Sophie Gouy, Jean-Philippe Métaxian, Geological Department, Lomonosov Moscow State University (MSU), Géosciences Environnement Toulouse (GET), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD), Institut des Sciences de la Terre d'Orléans (ISTO), Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS), Institut des Sciences de la Terre d'Orléans - UMR7327 (ISTO), Centre National de la Recherche Scientifique (CNRS)-Université d'Orléans (UO)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), Museum Geologi, Institut de Recherche pour Développement, Institut des Sciences de la Terre (ISTerre), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDE.MCG]Environmental Sciences/Global Changes, [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology
Abstract

International audience; Syn-eruptive monitoring of volcanic fluid, pyroclastic and aerosol phases is one of the main approaches to predict explosivity and duration of a current eruptive event. To constrain the fluid source as well as the pre- and syn-eruptive evolution of fluid - magma system of the 2010 Merapi volcano (Indonesia), we investigated the chemistry of pyroclasts and ashes collected on northern and western slopes on 9th November 2010 (originated from 5th to 6th November 2010 ash falls). The samples have been polished and investigated using a scanning electron microscopes (coupled with the automatic analyzer of particles by the program "Esprit" working in the module "Feature" in GET). Major element compositions were determined by electron microprobe analysis using two CAMECA SX50 equipped with SAMX automation and wavelength-dispersive spectrometer (WDS) in GET and ISTO. The first data demonstrates common presence of quartz (0.2 - 0.3 %), plagioclase (51 - 60%), alkaline feldspar (3 - 5%), olivine (0.02 - 0.2 %), orthopyroxene (1 - 4 %), clinopyroxene (10 - 16%), Fe-Ti oxides (5 - 10%), F-Cl apatite (0.02 - 0.6 %) in all four basaltic andesite ash samples. Regular association of olivine with quartz suggests thermodynamic disequilibrium attributable to magma mixing or/and contamination processes, as already observed at Merapi. Moreover, strong compositional variations of the residual glasses (3 - 9%) in major elements (SiO2 from 55 to 71 wt% and MgO from 0.2 to 3.3 wt%), minor components (Cl from 0.04 to 0.20 wt%; MnO from 0.01 to 0.3 wt%), and microlites reflect high heterogeneities, likely due to magma mixing processes.

Published
2011

37. Source geometry from exceptionally high resolution long period event observations at Mt Etna during the 2008 eruption

Author

Louis De Barros, Gareth S. O'Brien, Luciano Zucarello, Domenico Patanè, Jean-Philippe Métaxian, Gilberto Saccorotti, Ivan Lokmer, Christopher J. Bean, School of Geological Sciences [Dublin], University College Dublin [Dublin] (UCD), Istituto Nazionale di Geofisica e Vulcanologia – Sezione di Pisa (INGV), Istituto Nazionale di Geofisica e Vulcanologia, Istituto Nazionale di Geofisica e Vulcanologia - Sezione di Catania (INGV), Laboratoire de Géophysique Interne et Tectonophysique (LGIT), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Laboratoire Central des Ponts et Chaussées (LCPC)-Institut des Sciences de la Terre (ISTerre), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut national des sciences de l'Univers (INSU - CNRS)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), Institut des Sciences de la Terre [2011-2015] (ISTerre [2011-2015]), Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Central des Ponts et Chaussées (LCPC)-Observatoire des Sciences de l'Univers de Grenoble [1985-2015] (OSUG [1985-2015]), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Central des Ponts et Chaussées (LCPC)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], Lava, Location, [SDE.MCG]Environmental Sciences/Global Changes, FOS: Physical sciences, High resolution, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], 010502 geochemistry & geophysics, 01 natural sciences, Physics - Geophysics, Impact crater, Long period, 0105 earth and related environmental sciences, Event (probability theory), Volcano seismology, Long Period Events, Ranging, Geophysics (physics.geo-ph), Geophysics, Magma, General Earth and Planetary Sciences, Upwelling, Mt Etna volcano, Mount Etna, Geology, Seismology
Abstract

International audience; During the second half of June, 2008, 50 broadband seismic stations were deployed on Mt Etna volcano in close proximity to the summit, allowing us to observe seismic activity with exceptionally high resolution. 129 long period events (LP) with dominant frequencies ranging between 0.3 and 1.2 Hz, were extracted from this dataset. These events form two families of similar waveforms with different temporal distributions. Event locations are performed by cross-correlating signals for all pairs of stations in a two-step scheme. In the first step, the absolute location of the centre of the clusters was found. In the second step, all events are located using this position. The hypocentres are found at shallow depths (20 to 700 m deep) below the summit craters. The very high location resolution allows us to detect the temporal migration of the events along a dike-like structure and 2 pipe shaped bodies, yielding an unprecedented view of some elements of the shallow plumbing system at Mount Etna. These events do not seem to be a direct indicator of the ongoing lava flow or magma upwelling.

Published
2009
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38. Crustal structure below Popocatépetl Volcano (Mexico) from analysis of Rayleigh waves

Author

Jean-Philippe Métaxian, Philippe Lesage, Louis De Barros, C. Valdés-González, Helle Pedersen, Laboratoire de Géophysique Interne et Tectonophysique (LGIT), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Laboratoire Central des Ponts et Chaussées (LCPC)-Institut des Sciences de la Terre (ISTerre), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut national des sciences de l'Univers (INSU - CNRS)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), Institut de Recherche pour le Développement (IRD), Instituto de Geofisica, Universidad Nacional Autónoma de México (UNAM), Laboratoire Central des Ponts et Chaussées (LCPC)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut des Sciences de la Terre [2011-2015] (ISTerre [2011-2015]), Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Central des Ponts et Chaussées (LCPC)-Observatoire des Sciences de l'Univers de Grenoble [1985-2015] (OSUG [1985-2015]), and Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)

Subjects
Diffraction, 010504 meteorology & atmospheric sciences, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], Inversion (geology), volcano seismology, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], 010502 geochemistry & geophysics, 01 natural sciences, Physics - Geophysics, symbols.namesake, Geochemistry and Petrology, Rayleigh wave, Dispersion (water waves), Rayleigh waves, 0105 earth and related environmental sciences, geography, geography.geographical_feature_category, Volcanic belt, Volcano seismology, Crust, [SDE.MCG.CPE]Environmental Sciences/Global Changes/domain_sde.mcg.cpe, Popocatépetl Volcano, crustal structure, Geophysics, Volcano, symbols, Phase velocity, Geology, Seismology
Abstract

International audience; An array of ten broadband stations was installed on the Popocatépetl volcano (Mexico) for four months between October 2002 and February 2003. 26 regional and teleseismic earthquakes were selected and filtered in the frequency time domain to extract the fundamental mode of the Rayleigh wave. The average dispersion curve was obtained in two steps. Firstly, phase velocities were measured in the period range [2–50] s from the phase difference between pairs of stations, using Wiener filtering. Secondly, the average dispersion curve was calculated by combining observations from all events in order to reduce diffraction effects. The inversion of the mean phase velocity yielded a crustal model for the volcano which is consistent with previous models of the Mexican Volcanic Belt. The overall crustal structure beneath Popocatépetl is therefore not different from the surrounding area, and the velocities in the lower crust are confirmed to be relatively low. Lateral variations of the structure were also investigated by dividing the network into four parts and by applying the same procedure to each sub-array. No well-defined anomalies appeared for the two sub-arrays for which it was possible to measure a dispersion curve. However, dispersion curves associated with individual events reveal important diffraction for 6 s to 12 s periods which could correspond to strong lateral variations at 5 to 10 km depth. © 2007 Elsevier B.V. All rights reserved

Published
2008
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39. Microseismic activity within a serac zone in an alpine glacier (Glacier d'Argenti'ere, Mont Blanc, France)

Author

Pierre-Francois Roux, David Marsan, Luc Moreau, Jean-Philippe Métaxian, Gareth S. O'Brien, Laboratoire de Géophysique Interne et Tectonophysique (LGIT), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Laboratoire Central des Ponts et Chaussées (LCPC)-Institut des Sciences de la Terre (ISTerre), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut national des sciences de l'Univers (INSU - CNRS)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), Seismology and Computational Rock Physics Laboratory, University College Dublin [Dublin] (UCD), Environnements, Dynamiques et Territoires de la Montagne (EDYTEM), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Central des Ponts et Chaussées (LCPC)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Laboratoire Central des Ponts et Chaussées (LCPC)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Seismometer, 010506 paleontology, 010504 meteorology & atmospheric sciences, MOTION, BENEATH, CALIFORNIA, ICEQUAKES, Magnitude (mathematics), 01 natural sciences, Passive seismic, Emissivity, Serac, [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology, Geomorphology, 0105 earth and related environmental sciences, Earth-Surface Processes, geography, Microseism, geography.geographical_feature_category, GEOMETRY, ICE, Glacier, EARTHQUAKES, [SDE.ES]Environmental Sciences/Environmental and Society, 13. Climate action, Seismic array, ARRAY, Geology
Abstract

A passive seismic study was carried out underneath Glacier d’Argentière, Mont Blanc, France, where an array of seismometers was installed in a subglacial access tunnel. The data show a very high emissivity from the glacier. Fracturing can be discriminated from serac falls using the signal characteristics. We apply seismic array methods to locate the sources of these signals, using a two-step grid search in the parameter space. Four clusters of activity are found close to the network, showing that this fracturing does not take place uniformly over the glacier, but rather in isolated small zones. We compute a local magnitude using regional earthquakes for calibration. The magnitudes follow a classical Gutenberg–Richter law in the rangeML= −3 to 0.15, showing that no characteristic size events dominate the process. We suggest that those spatial clusters of icequakes could reveal the heterogeneous nature of the friction at the base of the glacier, with patches of high frictional stresses locally generating intense fracturing within the ice mass.

Published
2008
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40. Exploring Arctic Transpolar Drift During Dramatic Sea Ice Retreat

Author

Georg Heygster, Marcel Nicolaus, Michael Offermann, Burghard Bruemmer, Eero Rinne, Peter Wadhams, Jeremy Wilkinson, Jean Festy, Jan W. Bottenheim, Jari Haapala, Christian Haas, René Forsberg, Sebastian Gerland, Matthieu Weber, Alfred Wegener, Martin Doble, Erko Jakobson, Jacques Grangeon, Susan Hanson, Kerry J. Claffey, Herve Le Goff, Timo Palo, Jean-Claude Gascard, Henriette Skourup, Jean-Philippe Métaxian, Lars Kaleschke, B. C. Elder, Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut de Physique du Globe de Paris (IPGP (UMR_7154)), and Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)

Subjects
Arctic sea ice decline, geography, geography.geographical_feature_category, Arctic dipole anomaly, Global warming, Arctic ice pack, Arctic geoengineering, Oceanography, Arctic, Climatology, [SDE]Environmental Sciences, Sea ice, General Earth and Planetary Sciences, Environmental science, Arctic ecology
Abstract

The Arctic is undergoing significant environmental changes due to climate warming. The most evident signal of this warming is the shrinking and thinning of the ice cover of the Arctic Ocean. If the warming continues, as global climate models predict, the Arctic Ocean will change from a perennially ice-covered to a seasonally ice-free ocean. Estimates as to when this will occur vary from the 2030s to the end of this century. One reason for this huge uncertainty is the lack of systematic observations describing the state, variability, and changes in the Arctic Ocean.

Published
2008
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41. Shallow velocity structure and seismic site effects at Arenal volcano, Costa Rica

Author

Mauricio M. Mora, Jacques Dorel, Philippe Lesage, Guillermo E. Alvarado, Bernard Valette, Jean-Philippe Métaxian, Carlos Leandro, Laboratoire de Géophysique Interne et Tectonophysique (LGIT), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Laboratoire Central des Ponts et Chaussées (LCPC)-Institut des Sciences de la Terre (ISTerre), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut national des sciences de l'Univers (INSU - CNRS)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-PRES Université de Grenoble-Institut de recherche pour le développement [IRD] : UR219-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR), Escuela Centroamericana de Geología, Universidad Nacional de Costa Rica, Observatorio Sismológico y Vulcanológico de Arenal y Miravalles, Instituto Costarricense de Electricidad, Observatoire de Physique du Globe de Clermont-Ferrand (OPGC), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Blaise Pascal - Clermont-Ferrand 2 (UBP), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Central des Ponts et Chaussées (LCPC)-Centre National de la Recherche Scientifique (CNRS), and Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, Frequency band, 010502 geochemistry & geophysics, 01 natural sciences, symbols.namesake, SPAC method, Geochemistry and Petrology, H/V spectral ratio, Arenal volcano, Seismic refraction, Rayleigh wave, Rayleigh scattering, seismic site effect, 0105 earth and related environmental sciences, Seismic site effects, Masaya volcano, Love wave, Geophysics, Amplitude, 13. Climate action, velocity structure, symbols, Phase velocity, volcanic seismology, Seismology, Geology
Abstract

International audience; We use the spatial autocorrelation (SPAC) method with improved inversion algorithms to estimate the Love and Rayleigh dispersion curves at two sites at the West and Northeast flanks of Arenal volcano, Costa Rica. At the West flank site, the Rayleigh waves phase velocities vary from 765 m s− 1 at 1 Hz to 300 m s− 1 at 12 Hz and those of Love waves between 780 and 295 m s− 1 in the same frequency band. At the Northeast flank site, the Rayleigh wave velocities range from 1386 to 300 m s− 1 and those of Love from 1983 to 315 m s− 1. From dispersion curves we derive shallow (< 400 m) P and S waves velocity models. 2D velocity models down to a depth of 150 m are also obtained by seismic refraction surveys along two radial profiles on the tephra apron at West and East flanks. They present strong vertical and lateral variations in the velocity and thickness of the layers. Strong variations in amplitude of the spectral peaks are observed for the seismic events along two radial arrays. These site effects are analysed using the H/V spectral ratio method and S-wave theoretical transfer functions. Results show that the wave amplifications are related to resonance effects of shallow structure (< 150 m) and occur only where impedance contrast with the deeper layers is strong enough. In contrast, almost no site effect are detected at the Masaya shield volcano, Nicaragua, where the structure is more homogeneous and mainly composed of lava flows. When a resonance of the shallow layers occurs, the correlation coefficients between close stations increase at the corresponding frequency. The site effects may thus produce spurious results with the SPAC method. The H/V spectral ratio, used in complement of the SPAC method, can help detecting the site effects and testing the plane layer hypothesis. Furthermore, the theoretical transfer functions calculated for the estimated velocity models is also useful to validate the models.

Published
2006
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42. Seismicity related to the glacier of Cotopaxi Volcano, Ecuador

Author

Sebastián Araujo, Jean-Philippe Métaxian, Philippe Lesage, and Mauricio M. Mora

Subjects
geography, geography.geographical_feature_category, Hydraulics, Glacier, Induced seismicity, law.invention, Geophysics, Volcano, law, General Earth and Planetary Sciences, Fluid phase, Ice caps, Seismology, Geology
Abstract

[1] Significant seismic activity is generally recorded on volcanoes covered by an icecap. This work was carried out in order to quantify the role of the glaciers in the generation of seismicity for Cotopaxi volcano. We compared the seismic activity registered on the glacier and on the rock near the snout of the north flank glacier. We focused on the analysis of low frequency events (

Published
2003
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43. Tensor-Based Learning Framework for Automatic Multichannel Volcano-Seismic Classification

Author

Antonio Augusto Teixeira Peixoto, Carlos Alexandre Rolim Fernandes, Pablo Eduardo Espinoza Lara, Adolfo Inza, Jerome I Mars, Jean-Philippe Metaxian, Mauro Dalla Mura, and Marielle Malfante

Subjects
Classification, machine learning (ML), multidimensional signal processing, tensor learning, volcano, volcano-seismic signals, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

This article proposes a supervised tensor-based learning framework for classifying volcano-seismic events from signals recorded at the Ubinas volcano, in Peru, during a period of great activity in 2009. The proposed method is fully tensorial, as it integrates the three main steps of the automatic classification system (feature extraction, dimensionality reduction, and classifier) in a general multidimensional framework for tensor data, joining tensor learning techniques such as the multilinear principal component analysis (MPCA) and the support tensor machine (STM). By exploiting the use of multiple multichannel triaxial sensors, operating simultaneously in two seismic stations, the tensor patterns are constructed as stations × channels × features. The multidimensional structure of the data is then preserved, avoiding the tensor vectorization that often leads to a feature vector with a large dimension, which increases the number of parameters and may cause the “curse of dimensionality.”Moreover, the array vectorization breaks down the multidimensional structure of the data, which usually leads to performance degradation. The results showed a good performance of the proposed multilinear classification system, significantly outperforming its vectorial counterparts. The best result was obtained with the STuM classifier along with the MPCA.

Published
2021
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44. Study of seismic site effects using H/V spectral ratios at Arenal Volcano, Costa Rica

Author

Guillermo E. Alvarado, Carlos Leandro, Mauricio M. Mora, Jacques Dorel, Philippe Lesage, Jean-Philippe Métaxian, Pierre-Yves Bard, Laboratoire de Géophysique Interne et Tectonophysique (LGIT), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Central des Ponts et Chaussées (LCPC)-Centre National de la Recherche Scientifique (CNRS), Observatoire de Physique du Globe de Clermont-Ferrand (OPGC), Université Blaise Pascal - Clermont-Ferrand 2 (UBP)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), and Institut de Recherche pour le Développement (IRD)

Subjects
geography, geography.geographical_feature_category, Ambient noise level, Mineralogy, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, Seismic site effects, Arenal volcano, Transfer function, Seismic wave, Physics::Geophysics, Geophysics, Volcano, Close relationship, [SDU]Sciences of the Universe [physics], General Earth and Planetary Sciences, Seismic refraction, Geology, Seismology
Abstract

International audience; By using data obtained with a linear array at Arenal volcano, we show that the H/V spectral ratio method can be profitably applied to detect site effects on volcanoes. Similar results are obtained when calculating spectral ratios with different types of seismo-volcanic signals (tremor, ambient noise, explosion quakes, LP events). We compare the H/V ratios with theoretical S-wave transfer functions calculated using velocity models obtained from seismic refraction studies. There is a good agreement when the H/V ratios display sharp peaks, indicating a close relationship between the ratios and the transfer function of the shallow structure. Furthermore, the main peaks of the spectral ratios are consistent with local amplification of seismic waves observed at the corresponding frequencies.

Published
2001
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45. Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Author

Pablo Eduardo Espinoza Lara, Carlos Alexandre Rolim Fernandes, Adolfo Inza, Jerome I. Mars, Jean-Philippe Metaxian, Mauro Dalla Mura, and Marielle Malfante

Subjects
Artificial intelligence, empirical mode decomposition, deconvolution, time domain analysis, spectral domain analysis, cepstral analysis, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

This article proposes the design of an automatic classifier using the empirical mode decomposition (EMD) along with machine learning techniques for identifying the five most important types of events of the Ubinas volcano, the most active volcano in Peru. The proposed method uses attributes from temporal, spectral, and cepstral domains, extracted from the EMD of the signals, as well as a set of preprocessing and instrument correction techniques. Due to the fact that multichannel sensors are currently being installed in seismic networks worldwide, the proposed approach uses a multichannel sensor to perform the classification, contrary to the usual approach of the literature of using a single channel. The presented method is scalable to use data from multiple stations with one or more channels. The principal component analysis method is applied to reduce the dimensionality of the feature vector and the supervised classification is carried out by means of several machine learning algorithms, the support vector machine providing the best results. The presented investigation was tested with a large database that has a considerable number of explosion events, measured at the Ubinas volcano, located in Arequipa, Peru. The proposed classification system achieved a success rate of more than 90%.

Published
2020
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46. Locating sources of volcanic tremor and emergent events by seismic triangulation: Application to Arenal volcano, Costa Rica

Author

Bernard Valette, Jean-Philippe Métaxian, and Philippe Lesage

Subjects
Atmospheric Science, Ecology, Paleontology, Soil Science, Triangulation (social science), Forestry, Probability density function, Volcanism, Aquatic Science, Oceanography, Standard deviation, Physics::Geophysics, Geophysics, Space and Planetary Science, Geochemistry and Petrology, Position (vector), Sliding window protocol, Earth and Planetary Sciences (miscellaneous), Slowness, Seismogram, Seismology, Geology, Earth-Surface Processes, Water Science and Technology
Abstract

[1] We address the issue of locating the sources of volcanic tremor and emergent events with a method requiring a limited amount of equipment. A network of several triangular seismic antennas made of vertical sensors is used. The slowness vectors are estimated at each array on a sliding window by inverting the time delays between the sensors calculated with the cross-spectral method. A probabilistic approach is adopted whereby each measure and its error are represented by a probability density function (PDF). A weighted summation of the PDFs is carried out in which the stable directions of propagation are enhanced. The effects of the structural heterogeneities are taken into account by introducing an additional error associated to a robust hyperbolic secant (sech)-type PDF. The resulting PDFs of the back-azimuth are combined to calculate a PDF of the source location. The maximum likelihood of this PDF is taken as an estimate of the source position and its spread is characterized by a covariance analysis. Data from an experiment carried out at Arenal volcano (Costa Rica) with four arrays are analyzed. The precision and robustness of the method are tested by exploring the influence of the array configuration and other parameters. The mean standard deviation on the position of the sources is 600 m for the tremor and 400 m for the explosions and long-period (LP) events. Several tremors, explosions and LP events are analyzed and their sources located. The seismogenic zone is located in a 600 m radius area centered on the active crater.

Published
2002
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47. Structure of Masaya and Momotombo volcano, Nicaragua, investigated with a temporary seismic network

Author

Stefan Wiemer, Jean-Philippe Métaxian, Francesco Grigoli, Irene Molinari, Wilfried Strauch, and Anne Obermann

Subjects
010504 meteorology & atmospheric sciences, Lava, volcanic unrest, Seismic noise, 010502 geochemistry & geophysics, 01 natural sciences, Gravity anomaly, Ambient seismic noise, Lava lake, Masaya, Momotombo, volcanic unrest, Tomography, Volcano, Impact crater, Geochemistry and Petrology, Crater lake, Masaya, Aftershock, 0105 earth and related environmental sciences, geography, Momotombo, geography.geographical_feature_category, Tectonics, Geophysics, 13. Climate action, Lava lake, Seismology, Geology, Momotombo, volcanic unrest, Lava lake, Masaya
Abstract

Since the end of 2013, the region around the two volcanoes Masaya and Momotombo, which includes the Nicaraguan capital Managua, has shown an unusually high seismic and volcanic activity. In December 2015, the Momotombo volcano erupted after 110 years of quiescence. Since mid-December 2015, the Masaya volcano has also shown gradually increasing activity, including the formation of a lava lake in its main crater. By adding 30 broadband stations, we had temporarily (December 2016–March 2017) densified the permanent Nicaraguan seismic network around these volcanoes to study the local seismicity and image the subsurface structure. During the observation period, we observed an overall low level of seismicity. Recorded events around Momotombo likely consist of aftershocks of the M5.5 earthquake that struck this area on September, 26th, 2016. At Masaya, we did not observe volcano-tectonic events. Using the continuous waveform recordings, we perform a 3D ambient seismic noise tomography that reveals a first image of the subsurface velocity structure below the Masaya and Momotombo volcanoes. While Momotombo shows a typical elongated low shear-wave velocity anomaly that reaches depths of about 8 km, Masaya does not show indications of a deep plumbing system. At Masaya, we have indications of a shallow (0–3 km) magmatic chamber, offset to the west and not directly below the active Santiago vent, where the crater lake is located At greater depth (3–8 km) a low velocity anomaly towards the northeast coincides in location with a modelled positive gravity anomaly and could indicate the presence of a former intrusive body. With this study we want to trigger further interest in the diverse tectonic and volcanic features of Nicaragua. Future, long-term seismic imaging and monitoring projects are of critical interest for the estimation of seismic and volcanic risks in Managua and the surroundings.

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48. High explosive 2010 Merapi eruption: Evidence for shallow-level crustal assimilation and hybrid fluid

Author

Anastassia Borisova, Caroline Martel, Sophie Gouy, Indyo Pratomo, Jean-Paul Toutain, Bindeman, I. N., Danyushevsky, L., Jean-Philippe Métaxian, Surono, S., Géosciences Environnement Toulouse (GET), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD), Institut des Sciences de la Terre d'Orléans - UMR7327 (ISTO), Centre National de la Recherche Scientifique (CNRS)-Université d'Orléans (UO)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), Museum Geologi, Institut de Recherche pour Développement, Geological Sciences, University of Oregon [Eugene], ARC Centre of Excellence in Ore Deposits, Tasmania University, Institut des Sciences de la Terre (ISTerre), Université Joseph Fourier - Grenoble 1 (UJF)-Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement [IRD] : UR219-PRES Université de Grenoble-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), and CVGHM

Subjects
[SDE.MCG]Environmental Sciences/Global Changes, [SDU.STU.VO]Sciences of the Universe [physics]/Earth Sciences/Volcanology
Abstract

International audience; The last eruption of the Merapi volcano (Central Java, Indonesia) was unexpectedly highly explosive. To better understand the processes controlling the magma-fluid system responsible for the paroxysmal subplinian eruption, we investigated first-stage tephra and pyroclastic flow samples collected in October - November 2010, and second-stage ashes sampled shortly after the 5 - 6th November 2010 paroxysmal subplinian eruption. Plagioclase phenocrysts from the pyroclastic flow and tephra reveal highly calcic cores (up to An94) and 18O values of 6.5 h which is in the range of 18O for the Merapi plagioclases and bulk rocks (Gertisser and Keller, 2003). The ash samples have strongly radiogenic 87Sr/86Sr ratios (0.70571-0.70575) approaching the compositional field of the 1998 calc-silicate xenoliths described by Deegan et al. (2010). The major and trace element compositions of silicic glasses and mineral phenocrysts, and O and Sr isotopic signatures of the plagioclases and the bulk rocks erupted in 2010 are explained by complete digestion of the 1998 calc-silicate xenoliths. The crustal xenoliths were assimilated in the 2010 basaltic andesite magma through mixing between a calcic melt ("crustal assimilant", CaO up to 10.5 wt%, CaO/Al2O3 up to 1.2) and a hydrous K-rich melt. Similarly to the 1998 calc-silicate xenoliths described earlier (Chadwick et al., 2007), the 2010 crustal assimilant was enriched in Mn (MnO up to 0.5 wt%), Sr, Zn and V. In contrast, the hydrous K-rich melt issued from a deeper magmatic source was enriched in volatiles (Cl up to 0.37 wt%, H2O+CO2 up to 6 wt%), and Al2O3, TiO2, and REE. This melt may have been saturated with an aqueous fluid at about 200 MPa, a pressure value consistent with the depth of the crustal assimilation. Experimental leaching of the ash samples evidences for the presence of an aqueous fluid enriched in Cl, Na, Ca, Cd, Sb and Zn during the paroxysmal eruption. Thus, 2010 eruption may have been caused by liberation of this NaCl-HCl-rich fluid from the pre-eruptive basaltic andesite magma. References: Chadwick J.P. et al. (2007). J. Petrology 48, 1793-1812. Deegan F.M. et al. (2010). J. Petrology 51, 1027-1051. Gertisser R., Keller J. (2003) J. Petrology 44, 475-489.

49. Detailed seismic imaging of Merapi volcano, Indonesia, from local earthquake travel-time tomography

Author

Andri Dian Nugraha, Jean-Philippe Métaxian, Andry Syaly Sembiring, Sri Widiyantoro, Nicholas Rawlinson, Said Kristyawan, Mohamad Ramdhan, Agus Budi-Santoso, Ahmad Ali Fahmi, Asep Saepuloh, Antoine Laurin, Widiyantoro, S [0000-0002-8941-7173], Nugraha, AD [0000-0002-4844-8723], and Apollo - University of Cambridge Repository

Subjects
geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Geophysical imaging, Merapi, Geology, sub-02, 010502 geochemistry & geophysics, Key features, 01 natural sciences, Travel time, DOMERAPI, Volcano, Magma, Magmatism, Tomography, Merbabu, Seismology, 0105 earth and related environmental sciences, Earth-Surface Processes
Abstract

© 2019 Elsevier Ltd Mt. Merapi, located in central Java, Indonesia, is one of the most active volcanoes in the world. It has been subjected to numerous studies using a variety of methods, including tomographic imaging, in an attempt to understand the structure and dynamics of its magmatic plumbing system. Results of previous seismic tomographic studies that include Mt. Merapi poorly constrain the location of its underlying magma source due to limited data coverage. In order to comprehensively understand the internal structure and magmatism of Mt. Merapi, a project called DOMERAPI was conducted, in which 53 broadband seismic stations were deployed around Mt. Merapi and its neighbourhood for approximately 18 months, from October 2013 to April 2015. In this study, we compare Vp, Vs, and Vp/Vs tomograms constructed using data obtained from local (DOMERAPI) and regional seismic networks with those obtained without DOMERAPI data. We demonstrate that the data from the DOMERAPI seismic network are crucial for resolving key features beneath the volcano, such as high Vp/Vs ratios beneath the Merapi summit at ∼5 km and ∼15 km depths, which we interpret as shallow and intermediate magma bodies, respectively. Furthermore, west-east vertical sections across Mt. Merapi, and a “dormant” (less active) volcano, Mt. Merbabu, exhibit high Vp/Vs and low Vp/Vs ratios, respectively, directly beneath their summits. This observation likely reflects the presence (for Mt. Merapi) and absence (for Mt. Merbabu) of shallow magma bodies near the surface.

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