Automatic phase picker for single component borehole seismic data with deep neural network

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Automatic phase picker for single component borehole seismic data with deep neural networkAuthors: Jing ZhengJerry M. HarrisBadr Al-RumaihDongzhuo LiJing ZhengChina University of Mining and Technology (Beijing)Stanford UniversitySearch for more papers by this author, Jerry M. HarrisStanford UniversitySearch for more papers by this author, Badr Al-RumaihStanford UniversitySearch for more papers by this author, and Dongzhuo LiStanford UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3215226.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractWith the development of deep learning techniques, researchers have studied some deep neural network models for auto-picking the polarization phase and the corresponding arrival time of events. However, the previous models need three component seismograms for the phase classification tasks. They are not suitable for processing borehole seismic monitoring data recorded by hydrophone arrays or distributed acoustic sensing (DAS) whose sensors are usually single component. In this work, we develop a single component data driven processing technique that can classify seismic phases (including the compressional wave P and the shear wave S) and auto-pick the arrival times for the corresponding phases. The technique is a combination of convolution neural network (CNN) and recurrent neural network (RNN) aimed at separating P-wave and S-wave from seismic waveforms with hundreds of receivers and sources. We evaluate the performance of our technique with field crosswell seismic recordings. The predicted P-wave and S-wave arrival times can achieve 99.83% and 93.50% agreement with the manual pickers given an error gap less than five time samples respectively, which shows that it is effective for single component crosswell seismic data processing. The technique can be also applied to other single component seismic processing.Presentation Date: Monday, September 16, 2019Session Start Time: 1:50 PMPresentation Time: 4:20 PMLocation: 221DPresentation Type: OralKeywords: machine learning, crosswell, compressional wave (P-wave), shear wave (S-wave), processingPermalink: https://doi.org/10.1190/segam2019-3215226.1FiguresReferencesRelatedDetailsCited byA Literature Review10 December 2021 SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Jing Zheng, Jerry M. Harris, Badr Al-Rumaih, and Dongzhuo Li, (2019), "Automatic phase picker for single component borehole seismic data with deep neural network," SEG Technical Program Expanded Abstracts : 2217-2221. https://doi.org/10.1190/segam2019-3215226.1 Plain-Language Summary Keywordsmachine learningcrosswellcompressional wave (P-wave)shear wave (S-wave)processingPDF DownloadLoading ...