Automation of passive seismic processing via machine learning and physics-informed methods

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyAutomation of passive seismic processing via machine learning and physics-informed methodsAuthors: Ivan Lim Chen NingLaura SwaffordMike CravenKevin DaviesEvan EarnestDean ThorntonIvan Lim Chen NingChevron Technical CenterSearch for more papers by this author, Laura SwaffordChevron Technical CenterSearch for more papers by this author, Mike CravenChevron Technical CenterSearch for more papers by this author, Kevin DaviesChevron Technical CenterSearch for more papers by this author, Evan EarnestChevron Technical CenterSearch for more papers by this author, and Dean ThorntonChevron Technical CenterSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3750116.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractAccurate seismic event locations are necessary for understanding induced seismicity. The first step in seismic event location processing is picking the arrival times of P and S waves recorded on all available seismic sensors. A machine learning workflow can ingest raw passive seismic data from multiple seismic sensors to identify P and S wave arrivals. Despite using machine learning algorithms for picking, considerable human oversight is required to ensure detection fidelity. This paper proposes a workflow consisting of machine learning and physics-informed methods to achieve total automation of passive seismic processing. We use PhaseNet, a deep-neural-network-based phase picking algorithm to pick seismic phases at a superhuman level. We apply an unsupervised learning method for data clustering to identify and group phases from the same seismic event. We also introduce a logic-based and novel physics-informed quality control (QC) workflow to ensure the detected events are physically plausible. Our QC method utilizes temporospatial-derived information to reject anomalous phase picks such as S phase picks with travel times shorter than the P phase picks. The ability to automatically and accurately identify phase arrivals reduces the time required to process passive seismic data.Keywords: machine learning, unsupervised, physics-informed, automation, processingPermalink: https://doi.org/10.1190/image2022-3750116.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Ivan Lim Chen Ning, Laura Swafford, Mike Craven, Kevin Davies, Evan Earnest, and Dean Thornton, (2022), "Automation of passive seismic processing via machine learning and physics-informed methods," SEG Technical Program Expanded Abstracts : 1521-1525. https://doi.org/10.1190/image2022-3750116.1 Plain-Language Summary Keywordsmachine learningunsupervisedphysics-informedautomationprocessingPDF DownloadLoading ...