An integrated workflow of improving the accuracy of first arrivals picking via deep learning

PreviousNext No AccessSecond International Meeting for Applied Geoscience & EnergyAn integrated workflow of improving the accuracy of first arrivals picking via deep learningAuthors: Yitao PuBo ZhangChenglin WeiYingyu XuHongfei LiuYitao PuThe University of AlabamaSearch for more papers by this author, Bo ZhangThe University of AlabamaSearch for more papers by this author, Chenglin WeiBureau of Geophysical ProspectingSearch for more papers by this author, Yingyu XuBureau of Geophysical ProspectingSearch for more papers by this author, and Hongfei LiuBureau of Geophysical ProspectingSearch for more papers by this authorhttps://doi.org/10.1190/image2022-3751804.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractRecently, first arrivals picking is treated as an image segmentation problem and deep learning (DL) algorithms have been successfully used to pick the first arrivals of seismic shot gathers. Researchers have demonstrated that cutting-edge DL architectures have the potential to improve the accuracy of first arrivals picking. However, it is impossible to realize 100% accuracy no matter how advanced the DL architecture is. Unfortunately, researchers put little effort into differentiating seismic traces, which have “accurate” first arrivals picking, from seismic traces that have “inaccurate” first arrivals picking. This paper regards the first arrival as the boundary in a binary image (shot gather) and proposes a workflow to pick the first arrivals of seismic shot gathers by analyzing the results computed using deep learning. Firstly, we compute a probability image by applying a model, which is trained using the Historically nested U-Net (HUnet), to the seismic shot gather. Then, we obtain a binary image by applying a threshold to the probability image. Next, we obtain “generation I” first arrivals by identifying the boundary in the binary image. The generation I first arrivals are “accurate” for some of the seismic traces while “inaccurate” for other seismic traces that are contaminated by noise. We designed a three-step workflow to extract the seismic traces that have “accurate” first arrivals and obtained “generation II” first arrivals. Finally, we compute the first arrivals for all seismic traces within shot gathers under the constraints of generation II first arrivals and achieve the final picking results-generation III first arrivals.Keywords: first arrivals picking, deep learning, quality controlPermalink: https://doi.org/10.1190/image2022-3751804.1FiguresReferencesRelatedDetails Second International Meeting for Applied Geoscience & EnergyISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2022 Pages: 3694 publication data© 2022 Published in electronic format with permission by the Society of Exploration Geophysicists and the American Association of Petroleum GeologistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 15 Aug 2022 CITATION INFORMATION Yitao Pu, Bo Zhang, Chenglin Wei, Yingyu Xu, and Hongfei Liu, (2022), "An integrated workflow of improving the accuracy of first arrivals picking via deep learning," SEG Technical Program Expanded Abstracts : 1925-1929. https://doi.org/10.1190/image2022-3751804.1 Plain-Language Summary Keywordsfirst arrivals pickingdeep learningquality controlPDF DownloadLoading ...