Wasserstein Distance-Based Full-Waveform Inversion With a Regularizer Powered by Learned Gradient
Author:
Affiliation:
1. Department of Mathematics, Harbin Institute of Technology, Weihai, China
2. School of Earth and Space Sciences, Institute of Artificial Intelligence, Peking University, Beijing, China
Funder
National Natural Science Foundation of China
Beijing Natural Science Foundation
Fundamental Research Funds for the Central Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/10006360/10035474.pdf?arnumber=10035474
Reference56 articles.
1. Parametric convolutional neural network-domain full-waveform inversion;wu;Geophysics,2019
2. Adjoint-Driven Deep-Learning Seismic Full-Waveform Inversion
3. Deep Learning-Based Low-Frequency Extrapolation and Impedance Inversion of Seismic Data
4. Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification
5. Reparameterized full-waveform inversion using deep neural networks
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