Multi-station volcano tectonic earthquake monitoring based on transfer learning

This study was partially supported by the Spanish FEMALE (PID 2019-106260GB-I00) and PROOF-FOREVER (EUR2022.134044) projects. This work has been partially supported by the project EUR 2022-134044 founded by MCIN/AEI/10.13039/501100011033 in the framework PROYECTOS "EUROPA EXCELENCIA" 2022, CORRESPONDIENTES AL PROGRAMA ESTATAL PARA AFRONTAR LAS PRIORIDADES DE NUESTRO ENTORNO, SUBPROGRAMA ESTATAL DE INTERNACIONALIZACION, DEL PLAN ESTATAL DE INVESTIGACION CIENTIFICA, TECNICA Y DE INNOVACION PARA EL PERIODO 2021-2023, EN EL MARCO DEL PLAN DE RECUPERACION TRANSFORMACION Y RESILIENCIAthe. English language editing was performed by Tornillo Scientific.Introduction: Developing reliable seismic catalogs for volcanoes is essential for investigating underlying volcanic structures. However, owing to the complexity and heterogeneity of volcanic environments, seismic signals are strongly affected by seismic attenuation, which modifies the seismic waveforms and their spectral content observed at different seismic stations. As a consequence, the ability to properly discriminate incoming information is compromised. To address this issue, multi-station operational frameworks that allow unequivocal real-time management of large volumes of volcano seismic data are needed.Methods: In this study, we developed a multi-station volcano tectonic earthquake monitoring approach based on transfer learning techniques. We applied two machine learning systems-a recurrent neural network based on long short-term memory cells (RNN-LSTM) and a temporal convolutional network (TCN)-both trained with a master dataset and catalogue belonging to Deception Island volcano (Antarctica), as blind-recognizers to a new volcanic environment (Mount Bezymianny, Kamchatka; 6 months of data collected from June to December 2017, including periods of quiescence and eruption).Results and discussion: When the systems were re-trained under a multi correlation-based approach (i.e., only seismic traces detected at the same time at different seismic stations were selected), the performances of the systems improved substantially. We found that the RNN-based system offered the most reliable recognition by excluding low confidence detections for seismic traces (i.e., those that were only partially similar to those of the baseline). In contrast, the TCN-based network was capable of detecting a greater number of events; however, many of those events were only partially similar to the master events of the baseline. Together, these two approaches offer complementary tools for volcano monitoring. Moreover, we found that our approach had a number of advantages over the classical short time average over long time-average (STA/LTA) algorithm. In particular, the systems automatically detect VTs in a seismic trace without searching for optimal parameter settings, which makes it a portable, scalable, and economical tool with relatively low computational cost. Moreover, besides obtaining a preliminary seismic catalog, it offers information on the confidence of the detected events. Finally, our approach provides a useful tentative label for subsequent analysis carried out by a human operator. Ultimately, this study contributes a new framework for rapid and easy volcano monitoring based on temporal changes in monitored seismic signals.Spanish FEMALE project PID 2019-106260GB-I00Spanish PROOF-FOREVER project EUR2022.134044MCIN/AEI EUR 2022-13404

Multi-station volcano tectonic earthquake monitoring based on transfer learning

Introduction: Developing reliable seismic catalogs for volcanoes is essential for investigating underlying volcanic structures. However, owing to the complexity and heterogeneity of volcanic environments, seismic signals are strongly affected by seismic attenuation, which modifies the seismic waveforms and their spectral content observed at different seismic stations. As a consequence, the ability to properly discriminate incoming information is compromised. To address this issue, multi-station operational frameworks that allow unequivocal real-time management of large volumes of volcano seismic data are needed.Methods: In this study, we developed a multi-station volcano tectonic earthquake monitoring approach based on transfer learning techniques. We applied two machine learning systems—a recurrent neural network based on long short-term memory cells (RNN–LSTM) and a temporal convolutional network (TCN)—both trained with a master dataset and catalogue belonging to Deception Island volcano (Antarctica), as blind-recognizers to a new volcanic environment (Mount Bezymianny, Kamchatka; 6 months of data collected from June to December 2017, including periods of quiescence and eruption).Results and discussion: When the systems were re-trained under a multi correlation-based approach (i.e., only seismic traces detected at the same time at different seismic stations were selected), the performances of the systems improved substantially. We found that the RNN-based system offered the most reliable recognition by excluding low confidence detections for seismic traces (i.e., those that were only partially similar to those of the baseline). In contrast, the TCN-based network was capable of detecting a greater number of events; however, many of those events were only partially similar to the master events of the baseline. Together, these two approaches offer complementary tools for volcano monitoring. Moreover, we found that our approach had a number of advantages over the classical short time average over long time-average (STA/LTA) algorithm. In particular, the systems automatically detect VTs in a seismic trace without searching for optimal parameter settings, which makes it a portable, scalable, and economical tool with relatively low computational cost. Moreover, besides obtaining a preliminary seismic catalog, it offers information on the confidence of the detected events. Finally, our approach provides a useful tentative label for subsequent analysis carried out by a human operator. Ultimately, this study contributes a new framework for rapid and easy volcano monitoring based on temporal changes in monitored seismic signals

Sistema de detección y clasificación de señales sísmico-volcánicas utilizando modelos ocultos de Markov (HMMS): aplicación a volcanes activos de Nicaragua e Italia

Tesis Univ. Granada. Programa Oficial de Doctorado en: Ciencias de la TierraEsta tesis doctoral ha sido parcialmente financiada por los siguientes proyectos: Ephestos, CGL2011-29499-C02-01; EC-FP7 Mediterranean Supersite Volcanoes (MED-SUV), proyecto regional "Grupo de Investigación en Geofísica y Sismología de la Junta de Andalucía, RNM104"; proyecto APASVO, TEC2012-315511 del Grupo de Investigación TIC-123, de la Junta de Andalucía