Deep Learning‐Based<i>H‐κ</i>Method (HkNet) for Estimating Crustal Thickness and<i>Vp/Vs</i>Ratio From Receiver Functions-Reference-Cited by-同舟云学术

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Deep Learning‐BasedH‐κMethod (HkNet) for Estimating Crustal Thickness andVp/VsRatio From Receiver Functions

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:

Wang Feiyi¹^ORCID,Song Xiaodong¹²^ORCID,Li Jiangtao³^ORCID

Affiliation:

1. Institute of Theoretical and Applied Geophysics School of Earth and Space Sciences Peking University Beijing China

2. Hebei Hongshan National Observatory on Thick Sediments and Seismic Hazards Beijing China

3. Department of Geophysics, School of Geodesy and Geomatics Wuhan University Wuhan China

Funder

National Natural Science Foundation of China

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/10.1029/2022JB023944

Reference56 articles.

1. High-resolution lithospheric structure beneath Mainland China from ambient noise and earthquake surface-wave tomography

2. Machine learning for data-driven discovery in solid Earth geoscience

3. Deep Relative Geologic Time: A Deep Learning Method for Simultaneously Interpreting 3‐D Seismic Horizons and Faults

4. Inverting Rayleigh surface wave velocities for crustal thickness in eastern Tibet and the western Yangtze craton based on deep learning neural networks

5. Crustal thickness (H) and Vp/Vs ratio (κ) images beneath the central Tien Shan revealed by the H-κ-c method

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4. Crustal Imaging with Noisy Teleseismic Receiver Functions Using Sparse Radon Transforms;Bulletin of the Seismological Society of America;2024-01-08

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