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Using deep learning to derive shear-wave velocity models from surface-wave dispersion data

Hu Jing, Hongrui Qiu, Zhang Haijiang and Yehuda Ben-Zion
Using deep learning to derive shear-wave velocity models from surface-wave dispersion data
Seismological Research Letters (February 2020) 91 (3): 1738-1751

Abstract

We present a new algorithm for derivations of 1D shear-wave velocity models from surface-wave dispersion data using convolutional neural networks (CNNs). The technique is applied for continental China and the plate boundary region in southern California. Different CNNs are designed for these two regions and are trained using theoretical Rayleigh-wave phase and group velocity images computed from reference 1D VS models. The methodology is tested with 3260 phase-group images for continental China and 4160 phase-group images for southern California. The conversions of these images to velocity profiles take approximately 23 s for continental China and approximately 30 s for southern California on a personal laptop with the NVIDIA GeForce GTX 1060 core and a memory of 6 GB. The results obtained by the CNNs show high correlation with previous studies using conventional methods. The effectiveness of the CNN technique makes this fast method an important alternative for deriving shear-wave velocity models from large datasets of surface-wave dispersion data.


ISSN: 0895-0695
EISSN: 1938-2057
Serial Title: Seismological Research Letters
Serial Volume: 91
Serial Issue: 3
Title: Using deep learning to derive shear-wave velocity models from surface-wave dispersion data
Affiliation: University of Science and Technology of China, School of Earth and Space Sciences, Hefei, China
Pages: 1738-1751
Published: 20200219
Text Language: English
Publisher: Seismological Society of America, El Cerrito, CA, United States
References: 64
Accession Number: 2020-039315
Categories: Seismology
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. sketch maps
N20°00'00" - N53°00'00", E74°00'00" - E135°00'00"
Secondary Affiliation: University of Southern California, USA, United States
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2020, American Geosciences Institute. Abstract, Copyright, Seismological Society of America. Reference includes data from GeoScienceWorld, Alexandria, VA, United States
Update Code: 202025
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