Genetic Nelder-Mead neural network algorithm for fault parameter inversion using GPS data

Abstract

The traditional genetic algorithm (GA) has unstable inversion results and is easy to fall into the local optimum when inverting fault parameters. Therefore, this article considers the combination of GA with other non-linear algorithms in order to improve the inversion precision of GA. This paper proposes a genetic Nelder-Mead neural network algorithm (GNMNNA). This algorithm uses a neural network algorithm (NNA) to optimize the global search ability of GA. At the same time, the simplex algorithm is used to optimize the local search capability of the GA. Through numerical examples, the stability of the inversion algorithm under different strategies is explored. The experimental results show that the proposed GNMNNA has stronger inversion stability and higher precision compared with the existing algorithms. The effectiveness of GNMNNA is verified by the Bodrum–Kos earthquake and Monte Cristo Range earthquake. The experimental results show that GNMNNA is superior to GA and NNA in both inversion precision and computational stability. Therefore, GNMNNA has greater application potential in complex earthquake environment.

Keywords

• Fault parameter inversion
• Genetic algorithm
• Nelder-Mead simplex algorithm
• Neural network algorithm