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Aims The aim of the study was to explore the suitability of MALDI-TOF MS for a rapid and correct identification of Mycobacterium avium subsp. paratuberculosis (MAP) field isolates. Methods and Results MALDI-TOF MS approach is becoming one of the most popular tests for the identification of intact bacterial cells which has been shown to be fast and reliable. For this purpose, 36 MAP field isolates were analysed through MALDI-TOF MS and the spectra compared with two different databases: one provided by the vendor of the system employed (Biotyper Version 3.0, Bruker Daltonics) and a homemade database containing spectra from both tuberculous and nontuberculous Mycobacteria. Moreover, Principal Component Analysis procedure was employed to confirm the ability of MALDI-TOF MS to discriminate between very closely-related subspecies. Our results suggest MAP can be differentiated from other Mycobacterium species, both when the species are very close (M. intracellulare) and when belonging to different subspecies (Mycobacterium avium subsp. avium and Mycobacterium avium subsp. silvaticum). Conclusions The procedure applied is fast, easy to perform, and achieves an earlier accurate species identification of MAP and nontuberculous Mycobacteria in comparison to other procedures. Significance and Impact of the Study The gold standard test for the diagnosis of paratuberculosis is still isolation of MAP by cultural methods, but additional assays, such as qPCR and sub-culturing for determination of mycobactin dependency are required to confirm its identification. We have provided here evidence pertaining to the usefulness of MALDI-TOF MS approach for a rapid identification of this mycobacterium among other members of Mycobacterium avium complex. This article is protected by copyright. All rights reserved.

Trabajo presentado en el European Space Agency Living Planet Symposium, celebrado en Praga (República Checa), del 9 al 13 de Mayo de 2016, Although the primary goal of the Swarm satellite mission is to measure the magnetic signals from the Earth, in order to better understand its planetary magnetic field, the SAFE (Swarm for Earthquake study) project (funded by ESA under the framework “STSE Swarm+Innovation”, 2014) aims at applying the new approach of geosystemics to the analysis of Swarm data for investigating the preparatory phase of earthquakes as seen from space. The main objective is to explore the possible link between magnetic/ionospheric anomalies and large earthquakes analysing Swarm as well as ground based data (seismic, magnetic, GNSS, etc.). This presentation shows the value of this approach for some earthquake case studies .

Tenerife is the central island of the Canary Archipelago Spain, which consists of seven islands that represent different stages of geological evolution. The Teide-Pico Viejo 28° 16′ 30′′ N, 16° 38′ 42′′ W stratocones formed during the last eruptive phase of the isle of Tenerife. It is an active, though currently quiescent, shield volcano that last erupted in 1909 and is located on the Tenerife Island. In the framework of the European Project FP6 Prevention, Information and Early Warning PREVIEW-EURORISK http://www.preview-risk.com/ , a field campaign was performed on Tenerife Island on September 2007. This campaign focused on the acquisition of in situ reflectance and emissivity spectra relative to Pico de Teide and Las Canadas Caldera. The collected spectra represented the ‘ground truth’ and have been used for the supervised classification on multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer ASTER and hyperspectral Earth Observing 1 EO1-Hyperion data. The first ever classification mapping carried out on the Teide Volcano by utilizing the remote-sensing method is reported in this article. The methods used to process and to classify the data are discussed, and a comparison with the existing geological maps is presented.

International audience; Dans le Commentaire su Matthieu (ComMt) d'Origène, Jésus joue un rôle central en tant que modèle d'exégète, notamment dans l'interprétation des paraboles. Cet article propose une analyse de ComMt 10, 2-3, où l'Alexandrin réfléchit sur l'interprétation de la parabole de l'ivraie donnée par Jésus en Mt 13, 37-43 : à la différence d'autres exégètes, Origène choisit d'interpréter même l'interprétation de la parabole. Nous étudierons la manière dont il utilise l'exégèse de Jésus pour justifier et construire la sienne : plus précisément, il apparaîtra que le développement de l'exégèse est profondément influencé par le sens « implicite » que l'Alexandrin attribue aux paroles du Christ.

Trabajo presentado en la EGU General Assembly 2016 (European Geosciences Union), celebrada en Viena (Austria) del 17 al 22 de abril de 2016., The Swarm satellite mission by ESA has the primary goal to measure the magnetic signals from the Earth to get new insights of the geomagnetic field and its sources. The SAFE ("Swarm for Earthquake study") project (funded by ESA in the framework "STSE Swarm+lnnovation", 2014) aims at applying the new approach of geosystemics to the analysis of Swarm satellite electromagnetic data for investigating the preparatory phase of earthquakes. The main objective of this project is to explore the possible link between magnetic ionospheric anomalies and large earthquakes analysing Swarm as well as ground based data (seismic, magnetic, GNSS, etc.). This work will show the state of the art in the study of lithosphere-atmosphere-ionosphere coupling (LAIC) together with some recent case studies.

Trabajo presentado en 35th General Assembly of the European Seismological Commission, celebrado en Trieste (Italia), del 4 al 10 de septiembre de 2016, The Swarm satellite mission by ESA has the primary goal to measure the magnetic signals from the Earth to get new insights of the geomagnetic field and its sources. The SAFE (“Swarm for Earthquake study”) project (funded by ESA in the framework "STSE Swarm+lnnovation", 2014) aims at applying the new approach of geosystemics to the analysis of Swarm satellite electromagnetic data for investigating the preparatory phase of large earthquakes. The main objective of the project is to explore the possible link between large earthquakes and precursory electromagnetic anomalies detected by Swarm and ground based data (seismic, magnetic, GNSS, etc.). This work will show some recent case studies analysed in the framework of the project.

This study describes an application of artificial neural networks for the recognition of flaming areas using hyper- spectral remote sensed data. Satellite remote sensing is considered an effective and safe way to monitor active fires for environmental and people safeguarding. Neural networks are an effective and consolidated technique for the classification of satellite images. Moreover, once well trained, they prove to be very fast in the application stage for a rapid response. At flaming temperature, thanks to its low excitation energy (about 4.34 eV), potassium (K) ionize with a unique doublet emission features. This emission features can be detected remotely providing a detection map of active fire which allows in principle to separate flaming from smouldering areas of vegetation even in presence of smoke. For this study a normalised Advanced K Band Difference (AKBD) has been applied to airborne hyper spectral sensor covering a range of 400-970 nm with resolution 2.9 nm. A back propagation neural network was used for the recognition of active fires affecting the hyperspectral image. The network was trained using all channels of sensor as inputs, and the corresponding AKBD indexes as target output. In order to evaluate its generalization capabilities, the neural network was validated on two independent data sets of hyperspectral images, not used during neural network training phase. The validation results for the independent data-sets had an overall accuracy round 100% for both image and a few commission errors (0.1%), therefore demonstrating the feasibility of estimating the presence of active fires using a neural network approach. Although the validation of the neural network classifier had a few commission errors, the producer accuracies were lower due to the presence of omission errors. Image analysis revealed that those false negatives lie in "smoky" portion fire fronts, and due to the low intensity of the signal. The proposed method can be considered effective both in terms of classification accuracy and generalization capability. In particular our approach proved to be robust in the rejection of false positives, often corresponding to noisy or smoke pixels, whose presence in hyperspectral images can often undermine the performance of traditional classification algorithms. In order to improve neural network performance, future activities will include also the exploiting of hyperspectral images in the shortwave infrared region of the electromagnetic spectrum, covering wavelengths from 1400 to 2500 nm, which include significant emitted radiance from fire.

Volcanic eruptions affect at different levels the population and economy of interested areas. Moreover, volcanic ash detection represents a key issue for aviation safety due to the harming effects on aircraft. For these reasons, an accurate and fast analysis of the data is needed to monitor the phenomena's evolution and to manage the risk mitigation phase. In this scenario, the introduction of an inversion approach based on Neural Networks (NNs) has significant interest to reduce the need of human interpretation of the ash detection maps as those generated by the application of brightness temperature difference approach. In this work we show that NNs algorithms are suitable for an accurate mapping of ash cloud on Moderate Resolution Imaging Spectroradiometer (MODIS) images in a very cloudy scenario as the ones of 2010 Eyjafjallajokull and 2011 Grimsvotn eruptions.

We relocate the 1990–1991 Potenza (Southern Apennines belt, Italy) sequences and calculate focal mechanisms. This seismicity clusters along an E–W, dextral strike–slip structure. Second-order clusters are also present and reflect the activation of minor shears. The depth distribution of earthquakes evidences a peak between 14 and 20 km, within the basement of the subducting Apulian plate. The analysed seismicity does not mirror that of Southern Apennines, which include NW–SE striking normal faults and earthquakes concentrated within the first 15 km of the crust. We suggest that the E–W faults affecting the foreland region of Apennine propagate up to 25 km of depth. The Potenza earthquakes reflect the reactivation of a deep, preexisting fault system. We conclude that the seismotectonic setting of Apennines is characterized by NW–SE normal faults affecting the upper 15 km of the crust, and by E–W deeper strike–slip faults cutting the crystalline basement of the chain.

In this work the volcanic ash and SO2 retrievals obtained by applying three different procedures (LUT - Look Up Table, NN - Neural Network and VPR - Volcanic Plume Removal) on MODIS TIR synthetic measurements have been compared. The synthetic measurements are generated using MODTRAN RTM for defined volcanic cloud configurations. The results, presented as the percentage difference between the retrieved values and the true values used for the synthetic data generation, indicate maximum differences of +/- 15% and +/- 10% for all the procedures and for ash and SO2 retrievals respectively. A sensitivity analysis has been also realized to investigate the influence of volcanic cloud altitude and water vapour profile on SO2 retrievals at 7.3 and 8.6 micron. Results confirm the high sensitivity of the 7.3 micron retrieval to the volcanic cloud altitude and show that the SO2 total masses estimated at 7.3 and 8.6 micron separately can be used to improve the information on the volcanic cloud altitude. Finally, the water vapour profile is used to compute the minimum altitude over which the 7.3 m retrieval is effective.

International audience; A systematic multi-parameter and multi-platform approach to study the slow process of earthquake preparation is fundamental to gain some insights on this complex phenomenon. In particular, an important contribution is the integrated analysis between ground geophysical data and satellite data. In this paper we review some of the more recent results and suggest the next directions of this kind of research. Our intention is not to detect a particular precursor but to understand the physics underlying the various observations and to establish a reliable physical model of the preparation phase before an impending earthquake. In this way, future investigation will search for suitable fore-patterns, which the physical model of multi-layers coupling predicts and characterizes by quasi-synchronism in time and geo-consistency in space. We also present alternative explanations for some anomalies which are not actually related to earthquakes, rather to other natural or anthropic processes.

This work proposes methodologies aimed at evaluating the sensitivity of optical and synthetic aperture radar (SAR) change features obtained from satellite images with respect to the damage grade due to an earthquake. The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010, located 25 km west–south–west of the city of Port-au-Prince. The disastrous shock caused the collapse of a huge number of buildings and widespread damage. The objective is to investigate possible parameters that can affect the robustness and sensitivity of the proposed methods derived from the literature. It is worth noting how the proposed analysis concerns the estimation of derived features at object scale. For this purpose, a segmentation of the study area into several regions has been done by considering a set of polygons, over the city of Port-au-Prince, extracted from the open source open street map geo-database. The analysis of change detection indicators is based on ground truth information collected during a postearthquake survey and is available from a Joint Research Centre database. The resulting damage map is expressed in terms of collapse ratio, thus indicating the areas with a greater number of collapsed buildings. The available satellite dataset is composed of optical and SAR images, collected before and after the seismic event. In particular, we used two GeoEye-1 optical images (one preseismic and one postseismic) and three TerraSAR-X SAR images (two preseismic and one postseismic). Previous studies allowed us to identify some features having a good sensitivity with damage at the object scale. Regarding the optical data, we selected the normalized difference index and two quantities coming from the information theory, namely the Kullback–Libler divergence (KLD) and the mutual information (MI). In addition, for the SAR data, we picked out the intensity correlation difference and the KLD parameter. In order to analyze the capability of these parameters to correctly detect damaged areas, two different classifiers were used: the Naive Bayes and the support vector machine classifiers. The classification results demonstrate that the simultaneous use of several change features from Earth observations can improve the damage estimation at object scale.

Spectral unmixing technique is used in remote sensed data analysis for the determination of certain basis spectra called 'endmembers'. Once those spectra are found, the image cube can be 'unmixed' into fractional abundance of each material in each pixel. In the present work infrared spectra recorded by Infrared Atmospheric Sounding Interferometer (IASI) were used to characterize the emission from Grimsvotn volcanic eruption on 2011. In particular, a methodology based on spectral unmixing theory was used in order to extract the spectral signature of volcanic cloud constituents, such as ash and sulphur dioxide (SO2) and maps of their abundances in a IASI image were obtained. Taking the advantage of IASI broad spectral coverage the broadband signature in the Thermal Infrared (TIR) radiance spectra in the 1000-1410 cm -1 range associated with the presence of aerosols was obtained. Volcanic ash and SO 2 spectral signatures were extracted, as well as those related to the simultaneous presence of ash, SO 2 and cloud. The study proved that spectral unmixing, applied to Hyperspectral images, is able to identify volcanic aerosols and other species like SO 2 despite a strong presence of meteorological clouds. Moreover, the analysis of hyperspectral datasets permitted to generate abundance maps for each endmember extracted. In particular, maps obtained for the test case of 2011 May, 23th put in evidence the separation between clouds of ejected SO 2 and volcanic ash. The former dispersed at Northern latitudes, whilst the latter was situated at southern latitudes, South of Iceland.

Transform-based lossy compression has a huge potential for hyperspectral (HS) data reduction. The emerging JPEG2000 technology is based on the synergistic use of both spectral and spatial compression techniques. In this context the choice of the spectral decorrelation approach can have a strong impact on the quality of the compressed image. Since hyperspectral images are highly correlated within each spectral band and in particular across neighboring frequency bands, the choice of a spectral decorrelation method that allows to retain as much information content as possible is desirable. From this point of view, several methods based on PCA and Wavelet have been presented in the literature. In this paper, we propose the use of Nonlinear Principal Component Analysis (NLPCA) transform as a lossy spectral compression method applied to hyperspectral data. Being the NLPCA the nonlinear generalization of the standard principal component analysis (PCA), it permits to represent in a lower dimensional space the same information content with less features than the standard PCA.

Artificial neural networks (ANNs) are a valuable and well-established inversion technique for the estimation of geophysical parameters from satellite images; once trained, they help generate very fast results. Furthermore, satellite remote sensing is a very effective and safe way to monitor volcanic eruptions in order to safeguard the environment and the people affected by those natural hazards. This paper describes an application of ANNs as an inverse model for the simultaneous estimation of columnar content and height of sulphur dioxide (SO2) plumes from volcanic eruptions using hyperspectral data from remote sensing. In this study two ANNs were implemented in order to emulate a retrieval model and to estimate the SO2 columnar content and plume height. ANNs were trained using all infrared atmospheric sounding interferometer (IASI) channels between 1000-1200 and 1300-1410 cm-1 as inputs, and the corresponding values of SO2 content and height of plume, obtained from the same IASI channels using the SO2 retrieval scheme by Carboni et al., as target outputs. The retrieval is demonstrated for the eruption of the Eyjafjallajokull volcano (Iceland) for the months of 2010 April and May and for the Grimsvotn eruption during 2011 May. Both neural networks were trained with a time series consisting of 58 hyperspectral eruption images collected between 2010 April 14 and May 14 and 16 images from 2011 May 22 to 26, and were validated on three independent data sets of images of the Eyjafjallajokull eruption, one in April and the other two in May, and on three independent data sets of the Grimsvotn volcanic eruption that occurred in 2011 May. The root mean square error (RMSE) values between neural network outputs and targets were lower than 20 Dobson units (DU) for SO2 total column and 200 millibar (mb) for plume height. The RMSE was lower than the standard deviation of targets for the Grimsvotn eruption. The neural network had a lower retrieval accuracy when the target value was outside the values used during the training phase. © The Authors 2014. Published by Oxford University Press on behalf of The Royal Astronomical Society.

This work describes results of noise filtering techniques based on a Non-Linear Principal Component Analysis (NLPCA), applied to Hyperspectral CASI remote sensed data. NLPCA was carried out using Artificial Neural Networks (ANN) which are capable of modeling data non-linearity. NLPCA operated feature extraction on which original data were reconstructed, filtering the noise which affects first VNIR bands. Such a technique cleans VNIR bands, but does not affect the signal of other bands (SWIR). In order to appreciate improvements in classification due to noise filtering a supervised classification algorithm, based on Support Vector Machines (SVM), was applied to both original, noise filtered data, and NLPCA components (NLPCs). Results show that classification obtained using NLPCA-derived data is more accurate. Finally, SVM classification was also applied to fused images adding LiDAR Digital Surface Model (DSM) to the NLPCs and the original image in order to evaluate further improvements introduced by the dimensionality reduction offered by the NLPCA. Results show that the proposed method is able to improve the classification both by reducing dimensionality and suppressing the noise.

International audience; In this paper, we propose a new method aiming at reducing the noise in hyperspectral images. It is based on the nonlinear generalization of Principal Component Analysis (NLPCA). The NLPCA is performed by an auto associative neural network that have the hyperspectral image as input and is trained to reconstruct the same image at the output. Thanks to its bottleneck structure, the AANN forces the hyper spectral image to be projected in a lower dimensionality feature space where noise as well as both linear and nonlinear correlations between spectral bands are removed. This process permits to obtain enhancements in terms of hyperspectral image quality. Experiments are conducted on different real hyper spectral images, with different contexts and resolutions. The results are qualitatively and quantitatively discussed and demonstrate the interest of the proposed method as compared to traditional approaches.

The possibility offered by space-based sensors represents an irreplaceable resource for monitoring in near real time the eruption activities. The high revisit time of sensor like MODIS, seems to be the most effective way to mitigate the aviation hazard imaging the phenomenon evolution. In this work we propose a neural networks based approach to the volcanic ash mass retrieval. In comparison with the techniques based on radiative transfer models, the proposed algorithm has shown similar accuracy and faster computation. This issue can be of real interest to address the problems inherent the volcanic activity in short time. A set of MODIS images collected during the Eyjafjallajokull eruption, occurred from the 14th of April to the 23rd of May 2010, has been used to analyze the performance variations due to different selection of the algorithm inputs, i.e. the MODIS channels from visible to thermal infrared electromagnetic spectrum. The best wavelength sets for the retrieval of the ash mass, optical thickness and effective radius have been identified by means of neural network pruning algorithm.

Volcanic eruptions can inject large amounts (Tg) of gas and particles into the troposphere and, sometimes, into the stratosphere. Besides the main gases (H 2O, CO 2, SO 2 and HCl), volcanic clouds contain a mix of silicate ash particles in the size range from 0.1 ?m to 1 mm or larger. The interest in volcanic ash detection is high, particularly because it represents a serious hazard for air traffic. Particles with dimensions of several millimetres can damage the aircraft structure (windows, wings, ailerons), while particles less than 10 ?m may be extremely dangerous for the jet engines and are undetectable by the pilots during night or in low visibility conditions. Furthermore, ash detection represents a critical step towards quantitative retrievals of plume parameters. In this paper two different satellite techniques for volcanic cloud detection and tracking are compared, namely a water vapour corrected version of the brightness temperature difference (BTD-WVC) procedure and an implementation of the robust satellite technique, specifically configured for volcanic ash (RST ASH). The BTD method identifies volcanic ash clouds on the basis of the brightness temperature difference measured in two infrared spectral bands at around 11 and 12 ?m. To account for the atmospheric water vapour differential absorption in the 11-12 ?m spectral range, which tends to reduce (and in some cases completely mask) the BTD signal, a water vapour correction procedure has been developed (BTD-WVC), based on measured or synthetic atmospheric profiles. RST ASH instead, is based on the analysis of a time series of satellite records, aimed at identifying signal anomalies through an automatic unsupervised change detection step. To assess the performance of the BTD-WVC and RST ASH methods in detecting volcanic ash clouds, some eruptive events of Mt Etna, observed by the Advanced Very High Resolution Radiometer (AVHRR) sensor, have been analysed. The obtained results show a good agreement between the BTD-WVC and RST ASH techniques for all the considered images, in terms of pixels detected as 'ash affected' (i.e. the ash cloud area). In particular, compared to the traditional BTD procedure, the BTD-WVC and RST ASH techniques significantly improve volcanic ash cloud detection, both in daytime and night-time data, especially in the case of low ash loading.

The great eruption of the Icelandic Eyjafjallajokull volcano that occurred from the 14th of April to the 23rd of May 2010 injected large and dense ash clouds into the atmosphere, causing major international air traffic disruption worldwide.

[1] The Kasatochi 2008 eruption was detected by several infrared satellite sensors including Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Very High Resolution Radiometer (AVHRR), and Atmospheric Infrared Sounder (AIRS). In this work a comparison between the volcanic cloud SO2 and ash retrievals derived from these instruments has been undertaken. The SO2 retrieval is carried out by using both the 7.3 and 8.7 μm absorption features while ash retrieval exploits the 10–12 μm atmospheric window. A radiative transfer scheme is also used to correct the volcanic ash effect on the 8.7 μm SO2 signature. As test cases, three near-contemporary images for each sensor, collected during the first days of the eruption, have been analyzed. The results show that the volcanic SO2 and ash are simultaneously present and generally collocated. The MODIS and AVHRR total ash mass loadings are in good agreement and estimated to be about 0.5 Tg, while the AIRS retrievals are slightly lower and equal to about 0.3 Tg. The AIRS and MODIS 7.3 μm SO2 mass loadings are also in good agreement and vary between 0.3 and 1.2 Tg, while the MODIS ash corrected 8.7 μm SO2 masses vary between 0.4 and 2.7 Tg. The mass increase with time confirms the continuous SO2 injection in the atmosphere after the main explosive episodes. Moreover the difference between the 7.3 and 8.7 μm retrievals suggests a vertical stratification of the volcanic cloud. The results also confirm the importance of the ash correction; the corrected 8.7 μm SO2 total masses are less than 30–40% of the uncorrected values.

This work is an evaluation, to which degree geological information can be obtained from modern remote sensing systems like the multispectral ASTER or the hyperspectral Hyperion sensor for a volcanic region like Teide Volcano (Tenerife, Canary Islands). To account for the enhanced information content these sensors provide, hyperspectral analysis methods, incorporating for example Minimum Noise Fraction-Transformation (MNF) for data quality assessment and noise reduction as well as Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) for supervised classification, were applied. Ground Truth reflectance data were obtained with a FieldSpec Pro measurements campaign conducted during later summer of 2007 in the frame of the EC project PREVIEW (http://www.preview-risk.com/).

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER ) has operated since 19 December 1999 from NASA's Terra Earth-orbiting, sun synchronous satellite. Emissivity and temperature standard products are based on the TES algorithms and required periodical validation campaign. In the frame of the EC project PREVIEW (http://www.preview-risk.com/) a field campaign on Volcano Teide was carried on, from the 16th to 24th of September 2007, to validate and to integrate the satellite derived products services.

[1] On 6 April 2009, a Mw = 6.3 earthquake occurred in the central Apennines (Italy) damaging the city of L'Aquila and the surrounding country. We relocate the October 2008 to 6 April 2009 foreshocks and about 2000 aftershocks occurred between 6 and 30 April 2009 by applying a double-difference technique and determine the stress field from focal mechanisms. The events concentrate in the upper 15 km of the crust. Three main NW-SE to NNW-SSE striking, 30°–45° and 80°–90° dipping faults were activated during the seismic sequence. Among these, a normal fault and a thrust were reactivated with dip-slip movements in response to NE-SW extension. The structural maturity of the seismogenic fault system is lower than that displayed by other systems in southern Apennines because of the lower strain rate of the central sector of the chain with respect to the southern one. VP/VS increases progressively from October 2008 to the 6 April 2009 main shock occurrence along a NW-SE strike because of an increment in pore fluid pressure along the fault planes. Pore pressure diffusion controls the space-time evolution of aftershocks. A hydraulic diffusivity of 80 m2 s−1 and a seismogenic permeability of about 10−12 m2 suggest the involvement of gas-rich (CO2) fluids within a highly fractured medium. Suprahydrostatic, high fluid pressure (about 200 MPa at 10 km of depth) within overpressurized traps, bounded by preexisting structural and/or lithological discontinuities at the lower upper crust boundary, are required to activate the April 2009 sequence. Traps are the storage zone of CO2-rich fluids uprising from the underlying, about 20 km deep, metasomatized mantle wedge. These traps easily occur in extensional regimes like in the axial sector of Apennines but are difficult to form in strike-slip regimes, where subvertical faults may cross the entire crust. In the Apennines, fluids may activate faults responsible for earthquakes up to Mw = 5–6. Deep fluids more than tectonic stress may control the seismotectogenesis of accretionary wedges.

In this work the Thermal InfraRed (TIR) measurements of the Spin Enhanced Visible and Infrared Imager (SEVIRI)on board the Meteosat Second Generation (MSG) geosyncronous satellite, have been used to estimate the daily evolution of the SO2 columnar abundance and ash plume optical thickness, particle effective radius and total mass of Mt. Etna volcanic plume. As test case the 24 November 2006 eruption has been considered. SEVIRI is an optical imaging radiometer characterized by 12 spectral channels, a high temporal resolution (one image every 15 minutes) and a 9 km2 footprint. The instrument?s spectral range includes the 8.7 ?m band (channel 7) and the 10.8 and 12.0 ?m split window bands (channels 9 and 10) used respectively for SO2 retrieval and volcanic ash detection and retrievals. The SO2 columnar abundance is estimated by means of a Look-Up Table (LUT) least squares fit procedure applied to channel 7, while the ash detection and retrievals are carried out by using the Brightness Temperature Difference algorithm applied to channels 9 and 10. All the simulations needed for the retrievals have been realized using MODTRAN 4 radiative transfer model. The SEVIRI volcanic plume SO2 and ash retrievals have been compared with the results obtained by processing the data collected at 12:20 GMT by the MODIS sensor on board of Aqua satellite. Results show the ability of SEVIRI to recognize and estimate the daily trend of SO2 and ash in an eruptive plume; for the 24 November 2006 eruption, the SO2 and ash emissions started at about 4 and 8 GMT respectively and terminated simultaneously at about 14 GMT. The comparison between SEVIRI and MODIS retrievals indicate a general good agreement.