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Peng Jin, Shihang Feng, Youzuo Lin, Brendt Wohlberg, David Moulton, Erol Cromwell and Xingyuan Chen, "CycleFCN: A physics-informed data-driven seismic waveform inversion method", in SEG Technical Program Expanded Abstracts, doi:10.1190/segam2020-w13-05.1, Oct 2020


Physics-driven computational techniques, which suffer from ill-posedness and high computational cost, have long been the standard for solving full-waveform inversion. In this work, we develop a novel inversion technique that combines physicsdriven models with data-driven methodologies based on the fully convolutional neural network (FCN) architecture. We design a cycle-consistency loss to connect two FCN networks that are trained to incorporate both seismic forward and inverse modeling. To evaluate the performance of our new inversion technique, we conduct several experiments to compare our hybrid inversion method with pure data-driven techniques. The results show that our new methods significantly improve the performance by stabilizing the convergence of the training process, and therefore yield a higher inversion accuracy.

BibTeX Entry

author = {Peng Jin and Shihang Feng and Youzuo Lin and Brendt Wohlberg and David Moulton and Erol Cromwell and Xingyuan Chen},
title = {{CycleFCN}: A physics-informed data-driven seismic waveform inversion method},
year = {2020},
month = Oct,
urlpdf = {},
urlhtml = {},
booktitle = {{SEG} Technical Program Expanded Abstracts},
doi = {10.1190/segam2020-w13-05.1}