VSP first arrival picking method based on Unet++ convolutional neural network

PreviousNext No AccessSEG 2020 Workshop: 2nd SEG Borehole Geophysics Workshop, Virtual, 26–27 November 2020VSP first arrival picking method based on Unet++ convolutional neural networkAuthors: Yuanzhong ChenGuangmin HuYating WangXingmiao YaoGang YuYanpeng LiRui GuoJunjun WuJianhua HuangYuanzhong ChenUniversity of Electronic and Technology of ChinaSearch for more papers by this author, Guangmin HuUniversity of Electronic and Technology of ChinaSearch for more papers by this author, Yating WangUniversity of Electronic and Technology of ChinaSearch for more papers by this author, Xingmiao YaoUniversity of Electronic and Technology of ChinaSearch for more papers by this author, Gang YuBGP Inc., CNPC.Search for more papers by this author, Yanpeng LiBGP Inc., CNPC.Search for more papers by this author, Rui GuoBGP Inc., CNPC.Search for more papers by this author, Junjun WuBGP Inc., CNPC.Search for more papers by this author, and Jianhua HuangBGP Inc., CNPC.Search for more papers by this authorhttps://doi.org/10.1190/bhgp2020-23.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract In the process of VSP exploration, after the shot point produces VSP waves, the geophones arranged on the surface or underground begin to receive signals. The effective VSP waves received by the geophones first are called “first arrivals”. As the exploration terrain becomes more and more complex, the first-arrival waveform changes greatly, and various waves interfere with each other, the efficiency and accuracy of the traditional first-arrival picking algorithm are very limited. In order to solve the above problems, this paper proposes a VSP data first arrival picking method based on Unet++ convolutional neural network. The method includes, obtaining VSP data and the first arrival time of VSP data. Automatically making VSP data labels according to the first arrival time, and then extracting the VSP data training set, validation set and test set from the acquired VSP data. According to the specific characteristics of VSP data, modifying the structure and parameter adjustment of the Unet++ model to establish a deep learning network model suitable for VSP data. Improving Unet++ convolutional neural network model based on the VSP data training and validation set training to obtain the first arrival pick model. Using the trained first arrival picking model to pick up the data in the test set and getting the first arrival picking result. Keywords: neural networks, sampling, processing, wells, earthquakePermalink: https://doi.org/10.1190/bhgp2020-23.1FiguresReferencesRelatedDetails SEG 2020 Workshop: 2nd SEG Borehole Geophysics Workshop, Virtual, 26–27 November 2020ISSN (online):2159-6832Copyright: 2020 Pages: 167 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 30 Dec 2020 CITATION INFORMATION Yuanzhong Chen, Guangmin Hu, Yating Wang, Xingmiao Yao, Gang Yu, Yanpeng Li, Rui Guo, Junjun Wu, and Jianhua Huang, (2020), "VSP first arrival picking method based on Unet++ convolutional neural network," SEG Global Meeting Abstracts : 85-88. https://doi.org/10.1190/bhgp2020-23.1 Plain-Language Summary Keywordsneural networkssamplingprocessingwellsearthquakePDF DownloadLoading ...