Real-data earthquake localization using convolutional neural networks trained with synthetic data

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Real-data earthquake localization using convolutional neural networks trained with synthetic dataAuthors: Nicolas VinardGuy DrijkoningenEric VerschuurNicolas VinardTU DelftSearch for more papers by this author, Guy DrijkoningenTU DelftSearch for more papers by this author, and Eric VerschuurTU DelftSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3428107.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractContinuous seismic monitoring systems aid us to act on preventing strong earthquakes, such as induced by oil and gas extraction, deep geothermal systems and carbon sequestration. These systems provide the data to detect and locate such events and to determine their source magnitude and mechanism. Estimating the location of the source is one of the first parameters to be determined. Most source localization methods in the field of induced seismicity combine migration-based operators with grid-search algorithms. These are computationally intensive and therefore not instantaneous. To improve the time to locate events via grid-search based methods, we investigate the use of Convolutional Neural Networks (ConvNets) to reduce the search space. This is achieved by feeding the ConvNets with the waveforms and outputting a possible area/volume for the source location. Once the ConvNet is trained, it can produce the output almost in real-time. Therefore, it can be used by grid-search type approaches to focus the grid search over the smaller volume provided by the ConvNet. In this study we train ConvNets with synthetic data and apply them to field data. To our knowledge this is the first attempt of training ConvNets on synthetic data for the task of earthquake localization of field data.Presentation Date: Wednesday, October 14, 2020Session Start Time: 1:50 PMPresentation Time: 3:30 PMLocation: 351FPresentation Type: OralKeywords: machine learning, neural networks, monitoring, induced seismicity, 3DPermalink: https://doi.org/10.1190/segam2020-3428107.1FiguresReferencesRelatedDetailsCited byGeneralized neural network trained with a small amount of base samples: Application to event detection and phase picking in downhole microseismic monitoringXiong Zhang, Huihui Chen, Wei Zhang, Xiao Tian, and Fangdong Chu3 September 2021 | GEOPHYSICS, Vol. 86, No. 5Microseismic event location using artificial neural networksDenis Anikiev, Umair bin Waheed, František Staněk, Dmitry Alexandrov, and Leo Eisner1 September 2021Anomaly Classification for Earthquake Prediction in Radon Time Series Data Using Stacking and Automatic Anomaly Indication Function7 May 2021 | Pure and Applied Geophysics, Vol. 178, No. 5 SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Nicolas Vinard, Guy Drijkoningen, and Eric Verschuur, (2020), "Real-data earthquake localization using convolutional neural networks trained with synthetic data," SEG Technical Program Expanded Abstracts : 1576-1580. https://doi.org/10.1190/segam2020-3428107.1 Plain-Language Summary Keywordsmachine learningneural networksmonitoringinduced seismicity3DPDF DownloadLoading ...