Direct seismic waveform classification and first-break picking based on fully convolutional networks from shot gathers with missing traces

PreviousNext No AccessSEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing, China, 5–7 November 2019Direct seismic waveform classification and first-break picking based on fully convolutional networks from shot gathers with missing tracesAuthors: Yue ZhaoSanyi YuanTao XieWenjing SangShangxu WangYue ZhaoChina University of PetroleumSearch for more papers by this author, Sanyi YuanChina University of PetroleumSearch for more papers by this author, Tao XieGeophysical Research Institute, China Oilfield Services LimitedSearch for more papers by this author, Wenjing SangChina University of PetroleumSearch for more papers by this author, and Shangxu WangChina University of PetroleumSearch for more papers by this authorhttps://doi.org/10.1190/iwmg2019_07.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract First-break (FB) picking can provide traveltime information that is useful for seismic exploration. Due to the limitation of actual acquisition conditions, receiving sensors are often irregularly distributed, which cannot provide a complete FB information of a study area. The fully FB picking from seismic data with partially missing traces and/or poor-quality traces generally needs interpolation before picking. Moreover, most conventional FB picking methods ignore the spatial correlation of FBs, and require inputting sensitive attributes extracted from seismic data. The utilization of interpolation and sensitive attributes not only increases the computational cost, but also brings more uncertainties in FB picking. Therefore, we propose a direct waveform classification and FB-picking method based on fully convolutional networks (FCNs) from shot gathers with missing traces. Through training, the network can directly classify the seismic data with missing traces into two categories: effective wave and background noise. And the boundary between the two categories is the FB. The feasibility of the method is verified by testing on the data set containing the actual thousands of shots with missing traces. The generalized results show that even when the proportion of missing traces reaches up to 50%, the classification accuracy is over 97% and FBs including at the locations of missing traces can be picked with good lateral continuities. Keywords: traveltime, machine learning, neural networks, full-waveform inversion, seismic attributesPermalink: https://doi.org/10.1190/iwmg2019_07.1FiguresReferencesRelatedDetails SEG 2019 Workshop: Mathematical Geophysics: Traditional vs Learning, Beijing, China, 5–7 November 2019ISSN (online):2159-6832Copyright: 2020 Pages: 138 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 17 Jan 2020 CITATION INFORMATION Yue Zhao, Sanyi Yuan, Tao Xie, Wenjing Sang, and Shangxu Wang, (2020), "Direct seismic waveform classification and first-break picking based on fully convolutional networks from shot gathers with missing traces," SEG Global Meeting Abstracts : 26-30. https://doi.org/10.1190/iwmg2019_07.1 Plain-Language Summary Keywordstraveltimemachine learningneural networksfull-waveform inversionseismic attributesPDF DownloadLoading ...