Adaptive first arrival picking model with meta-learning

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Adaptive first arrival picking model with meta-learningAuthors: Pengyu YuanWenyi HuXuqing WuJiefu ChenHien Van NguyenPengyu YuanUniversity of HoustonSearch for more papers by this author, Wenyi HuAdvanced Geophysical Technology, IncSearch for more papers by this author, Xuqing WuUniversity of HoustonSearch for more papers by this author, Jiefu ChenUniversity of HoustonSearch for more papers by this author, and Hien Van NguyenUniversity of HoustonSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3420195.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractDeep neural networks have achieved great success in many areas including seismic data processing for subsurface imaging. Conventional first arrival picking is a very important laborintensive procedure sitting at the very early stage in the comprehensive seismic data processing workflow. Early attempts at first arrival picking automation by introducing DNNs have already shown encouraging results. However its performance drops dramatically when there is a data distribution shift in the target dataset. It is expensive and unwise to label numerous samples and retrain our model every time we are facing a new dataset. A good domain adaptation method is needed to accumulate and transfer the existing learned knowledge to the new datasets in an efficient way. The plain transfer learning is not designed to adapt efficiently to the target dataset. In this paper, we applied one of the state-of-art meta-learning approaches to solve the adaptation problem for first arrival picking among different seismic datasets. We also refined the workflow by creating new mini-tasks on the training dataset and including a small amount of samples from target samples into the metatraining. We used different field datasets as the target datasets in the experiments and find out that with only 10 labeled images from the target dataset, the model adapts very well and gives more accurate picking results compared with the plain transfer learning. To our best knowledge, this is the first work of using meta-learning in seismic image processing. We believe our work has great potential for enhancing the seismic data processing efficiency and reducing the turnaround time significantly.Presentation Date: Tuesday, October 13, 2020Session Start Time: 8:30 AMPresentation Time: 9:20 AMLocation: 351FPresentation Type: OralKeywords: machine learning, signal processing, seismic attributes, edge detection, near surfacePermalink: https://doi.org/10.1190/segam2020-3420195.1FiguresReferencesRelatedDetailsCited byExtraction of diffractions from seismic data using convolutional U-net and transfer learningSooyoon Kim, Soon Jee Seol, Joongmoo Byun, and Seokmin Oh27 January 2022 | GEOPHYSICS, Vol. 87, No. 2Imaging subsurface orebodies with airborne electromagnetic data using a recurrent neural networkMinkyu Bang, Seokmin Oh, Kyubo Noh, Soon Jee Seol, and Joongmoo Byun19 October 2021 | GEOPHYSICS, Vol. 86, No. 6 SEG Technical Program Expanded Abstracts 2020ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2020 Pages: 3887 publication data© 2020 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 30 Sep 2020 CITATION INFORMATION Pengyu Yuan, Wenyi Hu, Xuqing Wu, Jiefu Chen, and Hien Van Nguyen, (2020), "Adaptive first arrival picking model with meta-learning," SEG Technical Program Expanded Abstracts : 1486-1490. https://doi.org/10.1190/segam2020-3420195.1 Plain-Language Summary Keywordsmachine learningsignal processingseismic attributesedge detectionnear surfacePDF DownloadLoading ...