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A deep-learning-based approach for seismic surface-wave dispersion inversion (SfNet) with application to the Chinese mainland by Feiyi Wang; Xiaodong Song; Mengkui Li is a Earth and Planetary Sciences article available to read on EtoBox.

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We proposed a novel deep-learning method to invert surface-wave dispersions and obtained an updated v S model of the Chinese continent. • Synthetic models and flexible sampling strategy were used to build training dataset, which enhances greatly the usability of the method. • Synthetic tests and real-data application demonstrate that the method is robust and is superior to conventional methods in accuracy. A B S T R A C T Surface-wave tomography is an important and widely used method for imaging the crust and upper mantle velocity structure of the Earth. In this study, we proposed a deep learning (DL) method based on convolutional neural network (CNN), named SfNet, to derive the v S model from the Rayleigh wave phase and group velocity dispersion curves. Training a network model usually requires large amount of training datasets, which is labor-intensive and expensive to acquire. Here we relied on synthetics generated automatically from various spline-based v S models instead of directly using the existing v S models of an area to build the training dataset, which enhances the generalization of the DL method. In addition, we used a random sampling strategy of the dispersion periods

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Author

Feiyi Wang; Xiaodong Song; Mengkui Li

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Earth and Planetary Sciences (Physical Sciences)