Development of microseismic monitoring system using deep learning P- and S-waves picker in geothermal fields

PreviousNext No AccessProceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021Development of microseismic monitoring system using deep learning P- and S-waves picker in geothermal fieldsAuthors: Kyosuke OkamotoYusuke MukuhiraHiroshi AsanumaHäring MarkusKyosuke OkamotoNational Institute of Advanced Industrial Science and TechnologySearch for more papers by this author, Yusuke MukuhiraTohoku UniversitySearch for more papers by this author, Hiroshi AsanumaNational Institute of Advanced Industrial Science and TechnologySearch for more papers by this author, and Häring MarkusHäring GeoProjectSearch for more papers by this authorhttps://doi.org/10.1190/segj2021-048.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Microseismic monitoring in geothermal fields is a fundamental tool to estimate the extent of geothermal reservoirs, geothermal activity, flow paths in real-time. However, precise hypocenter determinations, which are the first step of the subsequent microseismic analyses, are based on manual picking of P- and S-wave arrivals by human analysts. This manual process is time and cost consuming. In this study, we demonstrated an automatic picker that was specified to microseismic events in geothermal fields. We developed an automatic picker based on deep learning with Okuaizu Geothermal Field (Japan) data. This deep learning model could read P- and S-wave arrivals with similar accuracy as manual reading by human analysts. Further, we applied this deep learning model to another geothermal field, Basel Geothermal Field (Switzerland). The deep learning model trained by the Okuaizu Geothermal Field data could provide P- and S- arrival times that can lead to qualitatively satisfiable hypocenter distributions for the Basel Geothermal Field data. Transfer learning with the Basel Geothermal Field data improved the accuracy of picking, especially for S-waves, and more precise hypocenter distributions were obtained. Further transfer learning using seismic data in many geothermal fields will lead to a more robust deep learning model for global microseismic monitoring. Keywords: deep learning, microseismic monitoring, geothermal energy, phase pickPermalink: https://doi.org/10.1190/segj2021-048.1FiguresReferencesRelatedDetails Proceedings of the 14th SEGJ International Symposium, Online, 18–21 October 2021ISSN (online):2159-6832Copyright: 2021 Pages: 349 publication data© 2021 Published in electronic format with permission by the Society of Exploration Geophysicists of JapanPublisher:Society of Exploration GeophysicistsSociety of Exploration Geophysicists of Japan HistoryPublished Online: 29 Nov 2021 CITATION INFORMATION Kyosuke Okamoto, Yusuke Mukuhira, Hiroshi Asanuma, and Häring Markus, (2021), "Development of microseismic monitoring system using deep learning P- and S-waves picker in geothermal fields," SEG Global Meeting Abstracts : 178-181. https://doi.org/10.1190/segj2021-048.1 Plain-Language Summary Keywordsdeep learningmicroseismic monitoringgeothermal energyphase pickPDF DownloadLoading ...