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Inversion of Fault Geometric Parameters Based on Mixture Density Networks: A Case Study of the 2013 Ms7.0 Lushan Earthquake in China

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Abstract

Mixture density networks (MDNs) are a class of neural network algorithms in the Bayesian framework that can invert for parameters and estimate the parameter uncertainties at the same time. We test MDNs by inverting the fault geometric parameters in a synthetic test with Okada’s model and demonstrate the efficiency and robustness of the model. To further describe the MDNs algorithm, we invert regional static GPS displacement data for the 2013 Ms7.0 Lushan Earthquake in China to obtain the fault geometric parameters. The inverted geometric parameters, which are in the form of distributions, agree well with the published solutions for this event once the 99.7% confidence interval are taken into account. The 99.7% confidence intervals of the strike and dip angles are [\(187.5^\circ\), \(220.4^\circ\)] and [\(20.76^\circ\), \(49.76^\circ\)], respectively. To demonstrate the applicability of the inverted geometric parameters, we invert the slip distribution of the Lushan earthquake with the inverted parameters and GPS displacements. From the slip distributions considering the distributions of the parameters, we obtain the 99.7% confidence intervals of the magnitude Mw, maximum slip and depth of maximum slip, which are [6.51, 6.65], [0.30, 0.74] m and [9.43, 13.61] km, respectively.

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Acknowledgements

This research is co-funded by the National Key Research and Development Plan of China under Grand No. 2018YFC1503604, the National Natural Science Foundation of China under Grand No. 41721003, and the DAAD Thematic Network Project (DAAD 57421148).

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Authors and Affiliations

  1. School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China

    Lixuan Zhou & Caijun Xu

  2. Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan, 430079, China

    Caijun Xu

  3. Collaborative Innovation Center of Geospatial Technology, Wuhan, 430079, China

    Caijun Xu

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  1. Lixuan Zhou
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Zhou, L., Xu, C. Inversion of Fault Geometric Parameters Based on Mixture Density Networks: A Case Study of the 2013 Ms7.0 Lushan Earthquake in China. Pure Appl. Geophys. 178, 21–38 (2021). https://doi.org/10.1007/s00024-020-02639-1

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  • DOI: https://doi.org/10.1007/s00024-020-02639-1

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