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• W2968049891 abstract "PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Multi-trace and multi-attribute analysis for first-break picking with the support vector machineAuthors: Xudong DuanJie ZhangXudong DuanUniversity of Science and Technology of China (USTC)Search for more papers by this author and Jie ZhangUniversity of Science and Technology of China (USTC)Search for more papers by this authorhttps://doi.org/10.1190/segam2019-3215554.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractMany algorithms have been proposed to automate the firstbreak picking, which, however, remains a challenging problem and still requires significant human efforts. We propose an SVM (support vector machine) method with multi-trace and multi-attribute analysis to improve the automatic picking. In this method, we still use some existing picking methods (e.g., short- and long-window ratios method) to obtain advanced attributes for training. Considering the substantial correlation in waveforms between adjacent traces, we also introduce three multi-trace attributes to incorporate spatial correlation constraints into our machine learning method. Due to the large similarity of data in the same project, the machine learning model needs to be trained on only a small amount of data but can be applied to infer most of the remaining data. We adopt the SVM technique in the training process to obtain a globally optimized model and introduce multi-trace constraints to refine picking results. We test our trained model on pseudosynthetic data generated by adding random Gaussian noise into high quality real data. The root-mean-square (RMS) picking error relative to ground truth ranges from 2.4 ms to 14 ms. We also apply our trained model to real data of dynamite sources and vibroseis sources, where the results demonstrate the effectiveness of the proposed method.Presentation Date: Wednesday, September 18, 2019Session Start Time: 9:20 AMPresentation Time: 11:25 AMLocation: Poster Station 2Presentation Type: PosterKeywords: machine learning, artificial intelligence, processing, statics, tomographyPermalink: https://doi.org/10.1190/segam2019-3215554.1FiguresReferencesRelatedDetailsCited byConvolution neural network application for first‐break picking for land seismic data28 June 2022 | Geophysical Prospecting, Vol. 70, No. 7Automatic first-arrival picking workflow by global path tracingDongliang Zhang, Tong W. Fei, Song Han, Constantine Tsingas, Yi Luo, and Hongwei Liu24 November 2021 | GEOPHYSICS, Vol. 87, No. 1First-Break Picking Classification Models Using Recurrent Neural Network15 December 2021First-arrival picking through fuzzy c-means and robust locally weighted regression16 July 2021 | Acta Geophysica, Vol. 69, No. 5VSP first arrival picking method based on Unet++ convolutional neural networkYuanzhong Chen, Guangmin Hu, Yating Wang, Xingmiao Yao, Gang Yu, Yanpeng Li, Rui Guo, Junjun Wu, and Jianhua Huang30 December 2020Automated arrival-time picking using a pixel-level networkYuanyuan Ma, Siyuan Cao, James W. Rector, and Zhishuai Zhang11 September 2020 | GEOPHYSICS, Vol. 85, No. 5 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 Xudong Duan and Jie Zhang, (2019), Multi-trace and multi-attribute analysis for first-break picking with the support vector machine, SEG Technical Program Expanded Abstracts : 2559-2563. https://doi.org/10.1190/segam2019-3215554.1 Plain-Language Summary Keywordsmachine learningartificial intelligenceprocessingstaticstomographyPDF DownloadLoading ..." @default.
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