Adaptive Feedback Convolutional‐Neural‐Network‐Based High‐Resolution Reflection‐Waveform Inversion-Reference-Cited by-同舟云学术

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Adaptive Feedback Convolutional‐Neural‐Network‐Based High‐Resolution Reflection‐Waveform Inversion

Published:2022-06 Issue:6 Volume:127 Page:
ISSN:2169-9313
Container-title:Journal of Geophysical Research: Solid Earth
language:en
Short-container-title:JGR Solid Earth

Author:

Wu Yulang¹²^ORCID,McMechan George A.³^ORCID,Wang Yanfei¹²⁴^ORCID

Affiliation:

1. Key Laboratory of Petroleum Resources Research Institute of Geology and Geophysics Chinese Academy of Sciences Beijing China

2. Innovation Academy for Earth Science Chinese Academy of Sciences Beijing China

3. The University of Texas at Dallas Richardson TX USA

4. University of Chinese Academy of Sciences Beijing China

Funder

National Natural Science Foundation of China

Chinese Academy of Sciences

University of Texas at Dallas

Publisher

American Geophysical Union (AGU)

Subject

Space and Planetary Science,Earth and Planetary Sciences (miscellaneous),Geochemistry and Petrology,Geophysics

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022JB024138

Reference61 articles.

1. Scattering-angle based filtering of the waveform inversion gradients

2. Deep-learning tomography

3. Inversion of normal incidence seismograms

4. Reverse time migration

5. Least-squares reverse time migration guided full-waveform inversion

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5. An Augmented Lagrangian Method-Based Deep Iterative Unrolling Network for Seismic Full-Waveform Inversion;IEEE Transactions on Geoscience and Remote Sensing;2024

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