Multistart Nelder-Mead Neural Network Algorithm for Earthquake Source Parameter Inversion of 2017 Bodrum-Kos Earthquake

摘要

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.