Pre-stack seismic inversion using SeisInv-ResNet

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Pre-stack seismic inversion using SeisInv-ResNetAuthors: Jiameng DuJunzhou LiuGuangzhi ZhangLei HanNing LiJiameng DuChina University of Petroleum (East China)Search for more papers by this author, Junzhou LiuSinopec Research InstituteSearch for more papers by this author, Guangzhi ZhangChina University of Petroleum (East China)Search for more papers by this author, Lei HanSinopec Research InstituteSearch for more papers by this author, and Ning LiChina Petroleum Logging CO.LTD.Search for more papers by this authorhttps://doi.org/10.1190/segam2019-3215750.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractDeep learning has a good performance in feature extraction and nonlinear fitting. In recent years, there has been more and more researchers applying deep learning to geophysics. In this work, we build a Resnet for pre-stack seismic inversion for azimuthal anisotropic medium, from which we obtain the P-wave impedance, S-wave impedance and rock physics parameters of fracture model. We use the SeisInv-ResNet to calculate the P-wave impedance, S-wave impedance and Schoenberg fracture rock physics parameters ΔN, ΔT. Comparing model value with inversion result, the work illustrates the feasibility of SeisInv-ResNet for prestack seismic inversion. The less the underground structure of work area varies, the less training data the network needs. Besides, we use data from oil field for SeisInv-ResNet inversion to train the network and get a good result. The application of ResNet has a profound meaning in seismic inversion.Presentation Date: Tuesday, September 17, 2019Session Start Time: 1:50 PMPresentation Time: 3:05 PMLocation: 221DPresentation Type: OralKeywords: machine learning, prestack, inversion, azimuth, HTIPermalink: https://doi.org/10.1190/segam2019-3215750.1FiguresReferencesRelatedDetailsCited byPrestack seismic inversion for elastic parameters using model-data-driven generative adversarial networksShuai Sun, Luanxiao Zhao, Huaizhen Chen, Zhiliang He, and Jianhua Geng20 February 2023 | GEOPHYSICS, Vol. 88, No. 2Multichannel seismic impedance inversion based on Attention U-Net27 February 2023 | Frontiers in Earth Science, Vol. 11Intelligent AVA Inversion Using a Convolution Neural Network Trained with Pseudo-Well Datasets30 January 2023 | Surveys in Geophysics, Vol. 86Seismic Impedance Inversion Using Conditional Generative Adversarial NetworkIEEE Geoscience and Remote Sensing Letters, Vol. 19AVO Inversion Based on Transfer Learning and Low-Frequency ModelIEEE Geoscience and Remote Sensing Letters, Vol. 19Semi-Supervised Learning for Seismic Impedance Inversion Using Generative Adversarial Networks28 February 2021 | Remote Sensing, Vol. 13, No. 5Semi-Supervised Deep Learning Seismic Impedance Inversion Using Generative Adversarial Networks 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 Jiameng Du, Junzhou Liu, Guangzhi Zhang, Lei Han, and Ning Li, (2019), "Pre-stack seismic inversion using SeisInv-ResNet," SEG Technical Program Expanded Abstracts : 2338-2342. https://doi.org/10.1190/segam2019-3215750.1 Plain-Language Summary Keywordsmachine learningprestackinversionazimuthHTIPDF DownloadLoading ...