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94 results on '"Girona, Társilo"'

4. The 2018 reawakening and eruption dynamics of Steamboat Geyser, the world's tallest active geyser.

Author

Reed, Mara H, Munoz-Saez, Carolina, Hajimirza, Sahand, Wu, Sin-Mei, Barth, Anna, Girona, Társilo, Rasht-Behesht, Majid, White, Erin B, Karplus, Marianne S, Hurwitz, Shaul, and Manga, Michael

Subjects
Steamboat, Yellowstone, geyser, hydrothermal, seasonaility
Abstract

Steamboat Geyser in Yellowstone National Park's Norris Geyser Basin began a prolific sequence of eruptions in March 2018 after 34 y of sporadic activity. We analyze a wide range of datasets to explore triggering mechanisms for Steamboat's reactivation and controls on eruption intervals and height. Prior to Steamboat's renewed activity, Norris Geyser Basin experienced uplift, a slight increase in radiant temperature, and increased regional seismicity, which may indicate that magmatic processes promoted reactivation. However, because the geothermal reservoir temperature did not change, no other dormant geysers became active, and previous periods with greater seismic moment release did not reawaken Steamboat, the reason for reactivation remains ambiguous. Eruption intervals since 2018 (3.16 to 35.45 d) modulate seasonally, with shorter intervals in the summer. Abnormally long intervals coincide with weakening of a shallow seismic source in the geyser basin's hydrothermal system. We find no relation between interval and erupted volume, implying unsteady heat and mass discharge. Finally, using data from geysers worldwide, we find a correlation between eruption height and inferred depth to the shallow reservoir supplying water to eruptions. Steamboat is taller because water is stored deeper there than at other geysers, and, hence, more energy is available to power the eruptions.

Published
2021

7. 2021 Volcanic activity in Alaska and the Commonwealth of the Northern Mariana Islands—Summary of events and response of the Alaska Volcano Observatory

Author

Orr, Tim R., primary, Dietterich, Hannah R., additional, Fee, David, additional, Girona, Társilo, additional, Grapenthin, Ronni, additional, Haney, Matthew M., additional, Loewen, Matthew W., additional, Lyons, John J., additional, Power, John A., additional, Schwaiger, Hans F., additional, Schneider, David J., additional, Tan, Darren, additional, Toney, Liam, additional, Wasser, Valerie K., additional, and Waythomas, Christopher F., additional

Published
2024
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9. Detection and Characterization of Seismic and Acoustic Signals at Pavlof Volcano, Alaska, Using Deep Learning.

Author

Tan, Darren, Fee, David, Witsil, Alex, Girona, Társilo, Haney, Matthew, Wech, Aaron, Waythomas, Chris, and Lopez, Taryn

Subjects
VOLCANIC eruptions, MACHINE learning, DEEP learning, VOLCANOES, EARTHQUAKE resistant design, CONVOLUTIONAL neural networks, SOIL vibration
Abstract

Volcanic tremor is a semi‐continuous seismic and/or acoustic signal that occurs at time scales ranging from seconds to years, with variable amplitudes and spectral features. Tremor sources have often been related to fluid movement and degassing processes, and are recognized as a potential geophysical precursor and co‐eruptive geophysical signal. Eruption forecasting and monitoring efforts need a fast, robust method to automatically detect, characterize, and catalog volcanic tremor. Here we develop VOlcano Infrasound and Seismic Spectrogram Network (VOISS‐Net), a pair of convolutional neural networks (one for seismic, one for acoustic) that can detect tremor in near real‐time and classify it according to its spectral signature. Specifically, we construct an extensive data set of labeled seismic and low‐frequency acoustic (infrasound) spectrograms from the 2021–2022 eruption of Pavlof Volcano, Alaska, and use it to train VOISS‐Net to differentiate between different tremor types, explosions, earthquakes and noise. We use VOISS‐Net to classify continuous data from past Pavlof Volcano eruptions (2007, 2013, 2014, 2016, and 2021–2022). VOISS‐Net achieves an 81.2% and 90.0% accuracy on the seismic and infrasound test sets respectively, and successfully characterizes tremor sequences for each eruption. By comparing the derived seismoacoustic timelines of each eruption with the corresponding eruption chronologies compiled by the Alaska Volcano Observatory, our model identifies changes in tremor regimes that coincide with observed volcanic activity. VOISS‐Net can aid tremor‐related monitoring and research by making consistent tremor catalogs more accessible. Plain Language Summary: Volcanic tremor is a persistent vibration of the ground, atmosphere, or both that can occur before and during volcanic eruptions. Despite its importance in volcano monitoring and eruption forecasting, volcano observatories do not have a reliable way of automatically detecting and identifying tremor due to the variable intensities and frequencies at which it occurs. In order to accomplish this, we develop and test a pair of machine learning models that classify spectrograms (i.e., images representing a signal's frequency content over time) from seismic and low‐frequency acoustic data. The models are trained on manually labeled images derived from the recent 2021–2022 eruption of Pavlof Volcano, Alaska, which demonstrated substantial signal diversity (e.g., different tremor types, earthquakes, explosions and noise). Our models achieve 81.2% and 90.0% accuracy on the seismic and low‐frequency acoustic test sets respectively, and perform well when applied to data recorded from past Pavlof Volcano eruptions. In addition, transitions in tremor sequences identified from our analysis generally coincide with shifts in eruptive patterns from Pavlof Volcano. Our tools can help volcano observatories systematically monitor tremor, and advance tremor research by making catalogs of their occurrences more consistent and accessible. Key Points: We develop a pair of convolutional neural networks that detect and classify volcano seismic and acoustic signalsWe apply our models to Pavlof Volcano eruptions (2007, 2013, 2014, 2016, and 2021–2022) and derive volcano seismoacoustic timelinesThe seismoacoustic timelines reveal shifts in unrest regimes linked to explosions and effusive activity [ABSTRACT FROM AUTHOR]

Published
2024
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17. Icy thermometers: how volcanic heat affects glacier mass balance

Author

Spagnolo, Matteo, primary, Howcutt, Stephen, additional, Rea, Brice, additional, Jaszewski, Jan, additional, Barr, Iestyn, additional, Coppola, Diego, additional, de Siena, Luca, additional, Girona, Társilo, additional, Gomez-Patron, Andie, additional, Mullan, Donal, additional, and Pritchard, Matthew, additional

Published
2023
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31. A quest for unrest in multiparameter observations at Whakaari/White Island volcano, New Zealand 2007–2018

Author

Caudron, Corentin, primary, Girona, Társilo, additional, Jolly, Arthur, additional, Christenson, Bruce, additional, Savage, Martha Kane, additional, Carniel, Roberto, additional, Lecocq, Thomas, additional, Kennedy, Ben, additional, Lokmer, Ivan, additional, Yates, Alexander, additional, Hamling, Ian, additional, Park, Iseul, additional, Kilgour, Geoff, additional, and Mazot, Agnès, additional

Published
2021
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32. Additional file 1 of A quest for unrest in multiparameter observations at Whakaari/White Island volcano, New Zealand 2007–2018

Author

Caudron, Corentin, Girona, Társilo, Jolly, Arthur, Christenson, Bruce, Savage, Martha Kane, Carniel, Roberto, Lecocq, Thomas, Kennedy, Ben, Lokmer, Ivan, Yates, Alexander, Hamling, Ian, Park, Iseul, Kilgour, Geoff, and Mazot, Agnès

Abstract

Additional file 1: Figure S1. Seismic velocity (single station cross-component approach) estimated using (a) the stretching and (b) the MWCS approaches between 0.1 and 1 Hz. The pink period in 2012–2013 delineates the magmatic eruption and the last two red lines mark the phreatic eruptions. The black and red lines correspond to the daily mean values across different combinations of components (EN, EZ and ZN) smoothed using a Hodrick–Prescott filter with different values, 500,000 and 100,000,000, respectively. See Fig. 4 for the description of the vertical lines corresponding to key time periods. Key periods are (1) 2011 earthquake swarm (2) dome extrusion (3) April 2016 eruption. Each tick on the x-axis corresponds to 1 January of each year.

Published
2021
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33. Additional file 2 of A quest for unrest in multiparameter observations at Whakaari/White Island volcano, New Zealand 2007–2018

Author

Caudron, Corentin, Girona, Társilo, Jolly, Arthur, Christenson, Bruce, Savage, Martha Kane, Carniel, Roberto, Lecocq, Thomas, Kennedy, Ben, Lokmer, Ivan, Yates, Alexander, Hamling, Ian, Park, Iseul, Kilgour, Geoff, and Mazot, Agnès

Subjects
Astrophysics::Earth and Planetary Astrophysics, Physics::Geophysics
Abstract

Additional file 2: Figure S2. Waveforms (left) and corresponding spectra (right) of the main classes of volcanic earthquakes. The amplitudes are normalized and a log scale if used for the x axes of the spectra.

Published
2021
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34. Additional file 3 of A quest for unrest in multiparameter observations at Whakaari/White Island volcano, New Zealand 2007–2018

Author

Caudron, Corentin, Girona, Társilo, Jolly, Arthur, Christenson, Bruce, Savage, Martha Kane, Carniel, Roberto, Lecocq, Thomas, Kennedy, Ben, Lokmer, Ivan, Yates, Alexander, Hamling, Ian, Park, Iseul, Kilgour, Geoff, and Mazot, Agnès

Abstract

Additional file 3: Figure S3. Compilation of the different observables during the time period studied (a) lake level height above the outlet (cyan; Christenson et al. 2017) and percentage of lake level (dark blue; see Hamling, (2017; 2020) for a description) (b) fumarole F0 (dark blue) and lake temperatures (cyan, Christenson et al. 2017) (c) SO2 flux measured with a DOAS (light blue lines and smoothed using a Hodrick Prescott filter in brown) and with a Cospec (magenta circles) (d) CO2 flux (red circles and smoothed line) and CO2/SO2 ratios (green circles) (e) RSAM (filtered between 2 and 5 Hz) computed using the seismic data at WIZ (black circles and smoothed version using a Hodrick Prescott filter in cyan). Each tick on the x-axis corresponds to 1 January of each year. Roman numbers correspond to different periods of reference described in the Chronology section.

Published
2021
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35. Additional file 4 of A quest for unrest in multiparameter observations at Whakaari/White Island volcano, New Zealand 2007–2018

Author

Caudron, Corentin, Girona, Társilo, Jolly, Arthur, Christenson, Bruce, Savage, Martha Kane, Carniel, Roberto, Lecocq, Thomas, Kennedy, Ben, Lokmer, Ivan, Yates, Alexander, Hamling, Ian, Park, Iseul, Kilgour, Geoff, and Mazot, Agnès

Abstract

Additional file 4: Figure S4. Time evolution of earthquake seismicity. Bars correspond to the total number of earthquakes of each type per week. Red bars denote eruptions and the pink section encompasses the unrest period from 2012 to 2014.The very-long period (VLP) families are from Park et al., 2020 (this issue). High frequency (RHF), volcano-tectonic (VT) and long-period earthquake (LP) characteristics are described in the text. Note that earthquake detections are not available for (a), (c) and (d) in 2018. Each tick on the x-axis corresponds to 1 January of each year. Roman numbers correspond to different periods of reference described in the Chronology section.

Published
2021
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36. Additional file 8 of A quest for unrest in multiparameter observations at Whakaari/White Island volcano, New Zealand 2007–2018

Author

Caudron, Corentin, Girona, Társilo, Jolly, Arthur, Christenson, Bruce, Savage, Martha Kane, Carniel, Roberto, Lecocq, Thomas, Kennedy, Ben, Lokmer, Ivan, Yates, Alexander, Hamling, Ian, Park, Iseul, Kilgour, Geoff, and Mazot, Agnès

Abstract

Additional file 8: Figure S8. Violin plot showing the yearly distribution of the 5-min main frequencies (dashed lines show median and interquartile). A kernel density estimator is used to show the distribution shape of the data; wider sections representing higher probabilities. Violin plots are useful to assess how data are distributed and if a single/multiple sources are activated.

Published
2021
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37. Additional file 9 of A quest for unrest in multiparameter observations at Whakaari/White Island volcano, New Zealand 2007–2018

Author

Caudron, Corentin, Girona, Társilo, Jolly, Arthur, Christenson, Bruce, Savage, Martha Kane, Carniel, Roberto, Lecocq, Thomas, Kennedy, Ben, Lokmer, Ivan, Yates, Alexander, Hamling, Ian, Park, Iseul, Kilgour, Geoff, and Mazot, Agnès

Abstract

Additional file 9: Figure S9. DSAR results since 2000. See Fig. 4 for the description of the vertical lines corresponding to key time periods. The 1-day DSAR data have been smoothed with a Hendrick Prescott filter, which prevents temporal aliasing (blue line (parameter = 1000) and orange (parameter = 108) lines). The few days of data in 1989 had DSAR around 1.

Published
2021
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38. Additional file 7 of A quest for unrest in multiparameter observations at Whakaari/White Island volcano, New Zealand 2007–2018

Author

Caudron, Corentin, Girona, Társilo, Jolly, Arthur, Christenson, Bruce, Savage, Martha Kane, Carniel, Roberto, Lecocq, Thomas, Kennedy, Ben, Lokmer, Ivan, Yates, Alexander, Hamling, Ian, Park, Iseul, Kilgour, Geoff, and Mazot, Agnès

Abstract

Additional file 7: Figure S7. Seismic velocity variations (single station cross-component approach) for the different combinations of components (EN, EZ and ZN). The 1-day results have been smoothed with a 30-day rolling mean. See Fig. 4 for the description of the vertical lines corresponding to key time periods.

Published
2021
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39. Additional file 6 of A quest for unrest in multiparameter observations at Whakaari/White Island volcano, New Zealand 2007–2018

Author

Caudron, Corentin, Girona, Társilo, Jolly, Arthur, Christenson, Bruce, Savage, Martha Kane, Carniel, Roberto, Lecocq, Thomas, Kennedy, Ben, Lokmer, Ivan, Yates, Alexander, Hamling, Ian, Park, Iseul, Kilgour, Geoff, and Mazot, Agnès

Abstract

Additional file 6: Figure S6. Correlation between lunar cycles and seismic amplitude for different window lengths. Faded colors correspond to measurements that are below the 5σ confidence level for correlation between the variables. Blue symbols are for positive correlation and red symbols are for negative correlation. See Fig. 4 for the description of the vertical lines corresponding to key time periods.

Published
2021
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40. Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures

Author

Lecocq, Thomas, Hicks, Stephen, Van Noten, Koen, Van Wijk, Kasper, Koelemeijer, Paula, De Plaen, Raphael, Massin, Frédérick, Hillers, Gregor, Anthony, Robert, Apoloner, Maria-Theresia, Arroyo-Solórzano, Mario, Assink, Jelle, Büyükakpınar, Pinar, Cannata, Andrea, Cannavo, Flavio, Carrasco, Sebastian, Caudron, Corentin, Chaves, Esteban, Cornwell, David, Craig, David, den Ouden, Olivier, Diaz, Jordi, Donner, Stefanie, Evangelidis, Christos, Evers, Läslo, Fauville, Benoit, Fernandez, Gonzalo, Giannopoulos, Dimitrios, Gibbons, Steven, Girona, Társilo, Grecu, Bogdan, Grunberg, Marc, Hetényi, György, Horleston, Anna, Inza, Adolfo, Irving, Jessica, Jamalreyhani, Mohammadreza, Kafka, Alan, Koymans, Mathijs, Labedz, Celeste, Larose, Éric, Lindsey, Nathaniel, McKinnon, Mika, Megies, Tobias, Miller, Meghan, Minarik, William, Moresi, Louis, Márquez-Ramírez, Víctor, Möllhoff, Martin, Nesbitt, Ian, Niyogi, Shankho, Ojeda, Javier, Oth, Adrien, Proud, Simon, Pulli, Jay, Retailleau, Lise, Rintamäki, Annukka, Satriano, Claudio, Savage, Martha, Shani-Kadmiel, Shahar, Sleeman, Reinoud, Sokos, Efthimios, Stammler, Klaus, Stott, Alexander, Subedi, Shiba, Sørensen, Mathilde, Taira, Taka'Aki, Tapia, Mar, Turhan, Fatih, van der Pluijm, Ben, Vanstone, Mark, Vergne, Jerôme, Vuorinen, Tommi, Warren, Tristram, Wassermann, Joachim, Xiao, Han, Büyükakpinar, Pinar, Rica, Costa, José, San, 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
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

50. Fractal degassing from Erebus and Mayon volcanoes revealed by a new method to monitor H2O emission cycles

Author

Aggangan, Brian, Costa, Fidel, Taisne, Benoit, Ildefonso, Sorvigenaleon, Girona, Társilo, and Earth Observatory of Singapore

Subjects
Science::Geology::Volcanoes and earthquakes [DRNTU]
Abstract

Many active volcanoes around the world release passively large amounts of gas between eruptions. Monitoring how these gas emissions fluctuate over time is crucial to infer the physical processes occurring within volcanic conduits and reservoirs. Here we report a new method to capture remotely the spectral properties of the emissions of H2O, the major component of most volcanic plumes. The method is based on a new theoretical model that correlates the volcanogenic water content of condensed volcanic plumes with the intensity of the light scattered by the droplets moving with the gas. In turn, we show that light intensity of the plume, and thus steam pulses time series, can be obtained with a proper analysis of digital images. The model is experimentally validated by generating condensed plumes with an ultrasonic humidifier, and then the method is applied to the gas plumes of Erebus and Mayon volcanoes. Our analysis reveals three main features: (1) H2O time series are composed of numerous periodic components of finite duration; (2) some periodic components are common in H2O and SO2 time series, but others are not; and (3) the frequency spectra of the H2O emissions follow a well-defined fractal distribution, that is, amplitude (Δ) and frequency (ν) are correlated by means of power laws (Δ ∝ νγ), with exponent γ ≈ − 1 for Erebus and for Mayon. These findings suggest that quiescent degassing emerges from the complex coupling between different processes occurring within magma plumbing systems. Our method is ideal for real-time monitoring of high-frequency H2O cycles at active volcanoes. Published version

Published
2015

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