TY - GEN
T1 - OPTIMAL TRANSPORT WITH A NEW PREPROCESSING FOR DEEP-LEARNING FULL WAVEFORM INVERSION
AU - Zhang, Hao
AU - Ma, Jianwei
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Full waveform inversion (FWI) has been implemented using deep learning techniques as an analogue recurrent neural network for geophysics. However, the cycle-skipping issue, from which the conventional FWI suffers, troubles the deep-learning aided FWI as well if the least-square loss function is used to measure the misfit between observed and synthetic data. We propose to use a Wasserstein distance loss function combined with a newly designed preprocessing transform, named integration affine scaling, for the inversion. This transform transfers the seismograms into probability densities, and significantly improves the inversion results. Numerical results show that the proposed method outperforms its counterparts in mitigating cycle-skipping, in comparison with other loss functions including the least-square, the absolute, and the quadratic Wasserstein distance losses.
AB - Full waveform inversion (FWI) has been implemented using deep learning techniques as an analogue recurrent neural network for geophysics. However, the cycle-skipping issue, from which the conventional FWI suffers, troubles the deep-learning aided FWI as well if the least-square loss function is used to measure the misfit between observed and synthetic data. We propose to use a Wasserstein distance loss function combined with a newly designed preprocessing transform, named integration affine scaling, for the inversion. This transform transfers the seismograms into probability densities, and significantly improves the inversion results. Numerical results show that the proposed method outperforms its counterparts in mitigating cycle-skipping, in comparison with other loss functions including the least-square, the absolute, and the quadratic Wasserstein distance losses.
KW - Optimal transport
KW - deep learning
KW - full waveform inversion
KW - integration affine transform
KW - loss functions
UR - https://www.scopus.com/pages/publications/85146688858
U2 - 10.1109/ICIP46576.2022.9897952
DO - 10.1109/ICIP46576.2022.9897952
M3 - 会议稿件
AN - SCOPUS:85146688858
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1446
EP - 1450
BT - 2022 IEEE International Conference on Image Processing, ICIP 2022 - Proceedings
PB - IEEE Computer Society
T2 - 29th IEEE International Conference on Image Processing, ICIP 2022
Y2 - 16 October 2022 through 19 October 2022
ER -