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• W2890715669 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2018Jump-starting neural network training for seismic problemsAuthors: Fantine HuotBiondo BiondiGregory BerozaFantine HuotStanford UniversitySearch for more papers by this author, Biondo BiondiStanford UniversitySearch for more papers by this author, and Gregory BerozaStanford UniversitySearch for more papers by this authorhttps://doi.org/10.1190/segam2018-2998567.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractDeep learning algorithms are immensely data-hungry and rely on large amounts of labeled data to achieve good performance. However the earth is intrinsically unlabeled and we are often confronted to fuzzy boundaries, uncertain labels, and absence of ground truth. Moreover, deep learning models do not always generalize well to conditions that are different from the ones encountered during training. In this context, it can be difficult to leverage deep learning algorithms for seismic problems. Herein we introduce strategies for overcoming these limitations, using synthetic data generation and transfer learning to jump-start the training of neural networks. We present this methodology through two case studies: earthquake detection using the Northern California Seismic Network (NCSN); and targeted noise filtering for ambient seismic noise recorded by a fiber optic array underneath Stanford campus.Presentation Date: Tuesday, October 16, 2018Start Time: 9:20:00 AMLocation: Poster Station 3Presentation Type: PosterKeywords: machine learning, neural networks, DAS (distributed acoustic sensors), earthquake, signal processingPermalink: https://doi.org/10.1190/segam2018-2998567.1FiguresReferencesRelatedDetailsCited byMicroseismic analysis over a single horizontal distributed acoustic sensing fiber using guided wavesAriel Lellouch, Bin Luo, Fantine Huot, Robert G. Clapp, Paige Given, Ettore Biondi, Tamas Nemeth, Kurt T. Nihei, and Biondo L. Biondi11 March 2022 | GEOPHYSICS, Vol. 87, No. 3A Literature Review10 December 2021Detecting microseismic events on DAS fiber with super-human accuracyFantine Huot, Ariel Lellouch, Paige Given, Robert G. Clapp, Biondo L. Biondi, Tamas Nemeth, and Kurt Nihei1 September 2021Elastic prestack seismic inversion through discrete cosine transform reparameterization and convolutional neural networksMattia Aleardi and Alessandro Salusti21 January 2021 | GEOPHYSICS, Vol. 86, No. 1Detecting earthquakes through telecom fiber using a convolutional neural networkFantine Huot and Biondo Biondi30 September 2020A Deep Transfer Learning Framework for Seismic Data Analysis: A Case Study on Bright Spot DetectionIEEE Transactions on Geoscience and Remote Sensing, Vol. 58, No. 5Automatic velocity analysis using convolutional neural network and transfer learningMin Jun Park and Mauricio D. Sacchi22 November 2019 | GEOPHYSICS, Vol. 85, No. 1Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis25 December 2019 | Remote Sensing, Vol. 12, No. 1Stratigraphy estimation from seismic data using deep learningFantine Huot, Robert Clapp, Biondo Biondi, Bruce Power, and Joe Stefani10 August 2019 SEG Technical Program Expanded Abstracts 2018ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2018 Pages: 5520 publication data© 2018 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 27 Aug 2018 CITATION INFORMATION Fantine Huot, Biondo Biondi, and Gregory Beroza, (2018), Jump-starting neural network training for seismic problems, SEG Technical Program Expanded Abstracts : 2191-2195. https://doi.org/10.1190/segam2018-2998567.1 Plain-Language Summary Keywordsmachine learningneural networksDAS (distributed acoustic sensors)earthquakesignal processingPDF DownloadLoading ..." @default.
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