Joint inversion for Vp, Vp/Vs of the San Fransico Bay Area using ADTomo
Ling Xia, Weiqiang Zhu, Huajian Yao

TL;DR
This paper introduces a novel joint inversion method combining fast sweeping and deep learning for seismic tomography in the San Francisco Bay Area, yielding more accurate and higher-resolution P wave and S wave velocity models.
Contribution
It develops a new joint inversion approach that improves velocity ratio accuracy and resolution using advanced phase picking and optimization techniques.
Findings
More reliable P wave to S wave velocity ratio obtained
Higher resolution P wave velocity models achieved
Velocity anomalies align with geological maps
Abstract
This article presents a new seismological tomography method based on the fast sweeping method and advanced seismic phase picking techniques to study the complex geological structures of the San Francisco Bay Area. By calculating the eikonal equation using the fast-sweeping method, this study obtains travel time information and gradient data under a given velocity structure. With an automatic differentiation algorithm to calculate gradients of the loss function and the L-BFGS algorithm to achieve optimization, the velocity model is iteratively adjusted to minimize the loss function. The P wave to S wave velocity ratio obtained through joint inversion is more reliable than the velocity ratio obtained directly by dividing the P wave and S wave velocity models. Compared to traditional inversion, the velocity ratio here does not require the same P wave and S wave travel path, thus improving…
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