seismic inversion

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2020Physics-guided self-supervised learning for low frequency data prediction in FWIAuthors: Wenyi HuYuchen JinXuqing WuJiefu ChenWenyi HuAdvanced Geophysical Technology Inc.Search for more papers by this author, Yuchen JinUniversity of HoustonSearch for more papers by this author, Xuqing WuUniversity of HoustonSearch for more papers by this author, and Jiefu ChenUniversity of HoustonSearch for more papers by this authorhttps://doi.org/10.1190/segam2020-3423396.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractIn many geophysical machine learning applications, labeled data are either too expensive or impossible to collect. In this work, we developed a physics-guided self-supervised learning method to predict the absent low frequency (LF) components in acquired seismic data to overcome the cycle-skipping issue in full waveform inversion (FWI). Similar to self-supervised learning in Natural Language Processing (NLP), this physics-guided label-free learning method consists of two stages: the pretext task and the downstream task. For the pretext task, we proposed two algorithms, the pseudo-LF (PLF) method and the sparse inversion-based bandwidth-extension method (BWE), to automatically generate the pseudo-labels from the input high frequency (HF) data attributes. Once the pre-designed deep learning network (DNN) model, a variant of the InceptV4 network, is pretrained within the pretext task stage, the learned knowledge is transferred to the second stage - the downstream task, where the Progressive Transfer Learning strategy is employed for fine-tuning the model in an iterative manner. Once again, in the downstream task stage, labels are automatically generated and updated by a physics-guided inversion procedure, rather than being externally and manually labeled. In the beginning of the workflow, the pseudo-LF or the bandwidth-extension algorithm is implemented to appr...