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Preprint of the article: "Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition" publshed in Seismological Research Letters-2019 (https://doi.org/10.1785/0220180334). This work is a 'deliverable' of theVULCAN.ears EC-funded project. This project has received funding from the European Union’s Horizon 2020 research andinnovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249](VULCAN.ears). &nbsp

Presentation ofthe milestone results achieved by theVULCAN.earsEU-funded project at the EGU.2019General Assembly, international conference under the title: ---------------------------------------------------------------------------------------------------------------------------- "Volcano-Independent Seismic Recognition: detecting and classifying events of a given volcano using data from others" Modern seismic networks provide a huge amount of data received in real-time, being impossible the manual identification of relevant events useful to monitor the activity of the volcano. Thus, many volcano observatories are interested in tools to perform an online, automatic analysis of the seismic activity. Machine Learning area provides various of Volcano-Seismic Recognition (VSR) systems designed to classify seismic events in real-time. However, only a few approaches can also detect them in a continuous data streams. Most of those VSR systems are based on the 2-step supervised paradigm: A training database (X-DB) of a given volcano ’X’ is prepared with hundreds of events manually detected and classified according to their physical origin. Statistical models are built analysing this DB, and are later used to automatically identify events in new data recorded at the volcano X. This supervised procedure is the major drawback to achieve a fast deployment of a VSR system for another volcano Y, as the preparation of its own Y-DB takes considerable time, and requires qualified operators and previous recordings, which is difficult for volcanoes without recent activity or which haven’t been monitored. In order to overcome these limitations, the EU-funded project ’VULCAN.ears’ focused on real-time, Volcano-Independent VSR (VI.VSR) approaches. It proposes alternative solutions based on state-of-the-art technologies as universal DBs and models, waveform standardisation and parallel architectures. Recent results obtained by mixing DBs from Popocatépetl, Colima, Deception and Arenal active volcanoes will be presented. We apply VULCAN.ears technologies to evaluate VSR systems on joint DBs built with data of several volcanoes. We also use volcano-independent models to automatically classify events of another volcano, analysing how the recognition accuracy varies as the training DB becomes more complex. All tests are carried out by an easy to use, user-friendly graphical application (geoStudio). All these achievements produce new insights useful to redesign the next-generation, portable and robust VSR systems. ---------------------------------------------------------------------------------------------------------------------------- The *.zip file includes both, the abstract communication and the poster presentation. This project has received funding from the European Union’s Horizon 2020 research andinnovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249](VULCAN.ears)

Presentations and abstracts of related with Volcano-Seismic Recognition (VSR), performed bytheVULCAN.ears- researching team and collaborators at the CoV.10 IAVCEI International Congress, Naples, Italy, 2018, including: ABSTRACTS: The VULCAN.ears project: an Esperanto for the volcano-seismic event recognition as a portable tool for real-time monitoring and eruption forecasting Methods of automatic recognition ofseismo-volcanic events: State-of-the-artand perspectives POSTERS Volcano-Seismic Recognition (VSR) under noisy conditions via waveform reconstruction TALKS The VULCAN.ears project: an Esperanto for the volcano-seismic event recognition as a portable tool for real-time monitoring and eruption forecasting . This project has received funding from the European Union’s Horizon 2020 research andinnovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249](VULCAN.ears)

International audience

El volcán Ubinas, considerado el más activo del Perú (Rivera et al., 2014); luego de cuatro años de inactividad desde la erupción del 2006-2009, el día 02 de setiembre de 2013 inicio una nueva fase eruptiva con explosiones freáticas. Esta actividad se incrementó durante los primeros días del mes de febrero de 2014, llegando a su mayor periodo de actividad durante el mes de abril de 2014, la actividad asociada a este nuevo proceso eruptivo continúa hasta la actualidad. Durante el proceso eruptivo 2013-2015 del volcán Ubinas, un intenso episodio de explosiones se observó entre el 29 de marzo al 11 de setiembre de 2014. En este mismo período, se han detectado 86 eventos de muy largo periodo (VLPs) que acompañaron a las explosiones mayores. Estos eventos VLP, se han presentado con periodos mayores a 5 segundos, con pocos ciclos y un rápido decaimiento de la coda. Este tipo de actividad se ha observado en muchos volcanes acompañando explosiones tipo Estrombolianas y Vulcanianas (Aster et al., 2003; Lesage et al. 2006, Inza et al., 2014) y se les ha asociado a posibles cambios volumétricos en una región específica del conducto volcánico. La característica de este tipo señales sísmicas, es que es posible determinar el ángulo de incidencia y el back-azimut del primer arribo de la onda, a partir delanálisis de movimiento de partículas, lo cual permite estimar con bastante precisión la fuente de este tipo de señales, estas localizaciones sirven para caracterizar las explosiones observadas durante el actual proceso eruptivo. Páginas 145-147.

International audience; Swarms of Long Period earthquakes generated by the Nyamuragira volcano for the period from January 2000 to November 2006 before 21 January 2000, 5 February 2001, 25 July 2002, 8 May 2004 and 27 November 2006 Nyamuragira eruptions have been analyzed. Interest is focused on the frequency distribution of these earthquakes and on the variation of the m-value of observed earthquake swarms. It is found that m-values which generally vary between 0.9 and 1.6, and shifts towards larger values of 1.7 to 3.23 two to four months prior to eruptions of the volcano.

International audience; We used the ambient noise cross-correlation and stretching methods to calculate variations in seismic velocities in the region of Volcán de Colima, Mexico. More than 15 years of continuous records were processed, producing long time series of velocity variations related to volcanic activity, meteorological effects, and earthquakes. Velocity variations associated with eruptive activity are tenuous, which probably reflects the open state of the volcano during the study period. Fifteen events among 26 regional tectonic earthquakes produced sharp, temporary decreases in seismic velocities, which then recovered progressively following a linear trend as a function of the logarithm of time. For the 15 events, the amplitude of the perturbation increased almost linearly with the logarithm of the amplitude of the seismic waves that shook the edifice. The most dramatic apparent velocity variation was a drop of up to 2.6% during the nearby M7.4 Tecomán earthquake in 2003. In order to locate the perturbation in the horizontal plane we applied an inverse method based on the radiative transfer approximation. We also used an original approach based on the frequency dependence of velocity variations to estimate the depth of the perturbation. Our results show that the velocity variation was well localized in the shallow layers (< 800 m) of the volcano, with almost no variations occurring outside the edifice. We discuss several possible interpretations and conclude that the most plausible explanation for the velocity decreases is the nonlinear elastic behavior of the granular volcanic material and its mechanical softening induced by transient strains.

International audience; In Proton Exchange Membrane Fuel Cells(PEMFC), Gas Diffusion Layer (GDL) has a major role inthe global system performances. This is the reason why manyresearch investigations try to model and optimize the GDLphysical properties. Currently, the major drawback of thesemodels is to obtain representative GDL mechanical and physicalinput parameters. In this paper, an experimental method usingDynamic Mechanical Analysis (DMA) is detailed to properlyobtain the GDL Young’s Modulus (or compression modulus) forhigh compressive loads under dynamic excitation.

El ascenso de magma es un episodio fundamental y característico en un proceso eruptivo como un signo inherente de inestabilidad del sistema magmático, lo cual puede ser detectado a varias escalas de tiempo (años, meses, y semanas). Este episodio es reflejado en la actividad sísmica, la cual se originada por una interacción dinámica entre gas, magma, y las rocas de un reservorio y/o conducto volcánico. El volcán Ubinas es considerado el más activo del Perú (Rivera et al., 2014). Luego de cuatro años de inactividad, volvió a entrar en actividad eruptiva el día 1 de setiembre de 2013 con explosiones freáticas. Esta actividad se reinició durante los primeros días del mes de febrero de 2014, la cual continúa hasta la actualidad con una fase magmática explosiva. A diferencia de las erupciones anteriores, en este proceso eruptivo, el trabajo de monitoreo permanente realizado por el Observatorio Vulcanológico del INGEMMET (OVI) permitió detectar con datos sísmicos, el día 17 de enero de 2014, el primer signo claro de ascenso de magma (presencia de sismos de tipo híbrido), es decir 3 meses antes de la fase explosiva mayor que se inició el día 13 de abril de 2014. Posteriormente y en base al estudio de los productos emitidos, correlacionados con datos de la actividad sísmica, ha sido posible estimar la profundidad de la cámara magmática y comprender el comportamiento del proceso eruptivo.

International audience; Arenal Volcano is a small (~1750 m above sea level, ~10 km3) stratovolcano that continuously erupted between July 1968 and October 2010. During this longlasting eruption (over 42 yr), a large volume of material--~5.6 × 108 m3 of dense rock equivalent--has been extruded and has produced a thick and extended lava fi eld, mainly on the western fl ank of the edifi ce. Measurements of ground deformation obtained using a network of dry-tilt stations are presented for the period 1986-2000. They show a continuous subsidence of the volcano with maximal amplitude on the western side. The load effect of the lava fi eld is calculated and explains the largest part of the observed tilts. Once the data are corrected by this load effect, pressure source models are not supported by the observations and by quality criteria on the models. Although the dry-tilt data from Arenal Volcano give limited constraints on the deformation models, they are representative of a long period of activity that cannot be recovered by other means. Moreover, the corresponding interpretative model is consistent with results obtained by geotechnical studies and modern ground deformation methods like interferometric synthetic aperture radar (InSAR).

International audience; Seismograms recorded at the receivers of a small-aperture seismic array usually display very similar waveforms and amplitudes, as a consequence of their close proximity. During the analysis of the volcanic tremor wave eld at Arenal volcano, Costa Rica, we detected signi cant di erences in the amplitudes of harmonic tremor recorded at the stations of a small-aperture (~210 m) seismic array. The amplitude distributions are geometrically complex and characterized by strong gradients. They occur just for harmonic tremors; any other type of seismic event produces nearly uniform amplitudes across the array. This suggests some relation with harmonic frequency content. Moreover, the spatial amplitude patterns change with time. Some of these observations could be explained by an extreme combination of source, path and site e ects. But they could be also produced by interference of di erent components of the seismic wave eld. We use numerical calculations to investigate the amplitude pattern generated by two interfering plane waves, and are able to reproduce the main features of the observed amplitude patterns. We propose mechanisms that might generate seismic wavefi elds with multiple components and conclude that interference can explain the complexity and variability of the harmonic tremor wave eld at Arenal volcano.