期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 19, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2020.3022021
关键词
Data models; Training; Geology; Computational modeling; Physics; Receivers; Adaptation models; Computational imaging; convolutional neural networks; data augmentation; full-waveform inversion (FWI); physics-consistent machine learning
类别
资金
- Center for Space and Earth Science, Los Alamos National Laboratory (LANL)
- Laboratory Directed Research and Development Program of LANL
Seismic full-waveform inversion is a computational imaging technique that provides detailed estimates of subsurface geophysical properties. This study develops a hybrid computational approach that combines physics-based models with data-driven methodologies to improve inversion accuracy. Validation using synthetic data demonstrates the method's accuracy and generalization ability.
Seismic full-waveform inversion (FWI) is a nonlinear computational imaging technique that can provide detailed estimates of subsurface geophysical properties. Solving the FWI problem can be challenging due to its ill-posedness and high computational cost. In this work, we develop a new hybrid computational approach to solve FWI that combines physics-based models with data-driven methodologies. In particular, we develop a data augmentation strategy that can not only improve the representativity of the training set but also incorporate important governing physics into the training process and, therefore, improve the inversion accuracy. To validate the performance, we apply our method to synthetic elastic seismic waveform data generated from a subsurface geologic model built on a carbon sequestration site at Kimberlina, California. We compare our physics-consistent data-driven inversion method to both purely physics-based and purely data-driven approaches and observe that our method yields higher accuracy and greater generalization ability.
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