3‐D S Wave Imaging via Robust Neural Network Interpolation of 2‐D Profiles From Wave‐Equation Dispersion Inversion of Seismic Ambient Noise
Author:
Affiliation:
1. School of Geophysics and Information Technology China University of Geosciences Beijing China
2. Deep Earth Imaging Future Science Platform CSIRO Kensington Perth Australia
3. Department of Physics University of Western Australia Perth Australia
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/10.1029/2022JB024663
Reference50 articles.
1. Aki K. &Richards P. G.(1980).Quantitative seismology: Thoery and methods.W.H. Freeman and Co.
2. Shear velocity structure of the Northland Peninsula, New Zealand, inferred from ambient noise correlations
3. A 3-D shear velocity model of the crust and uppermost mantle beneath the United States from ambient seismic noise
4. Seismic tomography with the reversible jump algorithm
5. Borisov D. Gao F. Williamson P. Simons F. J. &Tromp J.(2019).Robust surface‐wave full‐waveform inversion. Paper presented at the SEG International Exposition and Annual Meeting San Antonio Texas USA September 2019. Paper Number: SEG‐2019‐3215047.https://doi.org/10.1190/segam2019-3215047.1
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1. Space‐Time Monitoring of Seafloor Velocity Changes Using Seismic Ambient Noise;Journal of Geophysical Research: Solid Earth;2024-06
2. Joint wave-equation inversion of Rayleigh- and Love- dispersion curves;GEOPHYSICS;2023-12-14
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