Multistart Nelder–Mead Neural Network Algorithm for Earthquake Source Parameter Inversion of 2017 Bodrum–Kos Earthquake
Publication: Journal of Surveying Engineering
Volume 147, Issue 3
Abstract
A multistart Nelder–Mead neural network algorithm (multi NM-NNA) is presented, the purpose of which is to solve the problem that the existing nonlinear search algorithms are unstable when inversing earthquake source parameters with GPS data. Multi NM-NNA uses the nonuniform sampling strategy to generate the initial starting points to reduce manual intervention, and the Nelder–Mead simplex algorithm is used to optimize the local optimization capability of the NNA. Different GPS stations and fault types are simulated, and the NNA, hybrid particle swarm optimization (PSO)/simplex algorithm [multipeaks particle swarm optimization (MPSO)], and NM-NNA are used to perform earthquake source parameter inversion, respectively. The simulation experiment results show that the calculation precision of the NM-NNA is not affected by the number of stations, and it has better stability in the inversion of different fault types. Compared with the NNA and MPSO, the NM-NNA is more suitable for earthquake source parameter inversion, and the computational efficiency is higher than the NNA. The NNA, MPSO, NM-NNA, and multi NM-NNA are used to invert the earthquake source parameters of the Bodrum–Kos earthquake and carry out the precision estimation of the parameters. Experimental results show that the parameter estimates inverted by the multi NM-NNA are closer to the existing research results and have smaller standard deviation. It is shown that inversion uncertainty of the multi NM-NNA is lower, the calculation results are more stable, and the computational efficiency of the multi NM-NNA is higher than NNA. In the complex and changeable earthquake environment, the multi NM-NNA has greater application potential.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The types of raw data include the left lateral slip fault monitored by different GPS stations and different slip fault models, and can be provided as a MAT file.
Acknowledgments
The authors are grateful to all of the anonymous reviewers and editors for their careful review and valuable suggestions, which improved the quality of this paper. The authors thank Yingwen Zhao for his patient help. Part of the code used in this article comes from the MathWorks website, and some images were drawn using the open source software GMT. This research is supported by the National Natural Science Foundation of China (Nos. 41874001 and 41664001), National Key Research and Development Program (No. 2016YFB0501405), and Jiangxi Provincial Natural Science Foundation (20202BABL212015 and 20202BABL204070).
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Journal of Surveying Engineering
Volume 147 • Issue 3 • August 2021
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© 2021 American Society of Civil Engineers.
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Received: Oct 27, 2020
Accepted: Apr 16, 2021
Published online: Jun 10, 2021
Published in print: Aug 1, 2021
Discussion open until: Nov 10, 2021
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