Rayleigh wave dispersion inversion via modified sine cosine algorithm: Application to Hangzhou, China passive surface wave data

Abstract

To better address the inversion problem of Rayleigh wave dispersion data, this study proposes a modified sine cosine algorithm (MSCA). The sine cosine algorithm (SCA) is based on a combination of sine and cosine functions for optimization; however, its performance is limited by the selection of control parameters and the diversity of population evolution. To overcome these limitations, this study introduces a modified algorithm that incorporates an exponential update strategy and a novel offspring update strategy. First, the optimization performance of the original and the modified algorithm was validated through tests on ten complex benchmark functions. Then, the MSCA, the SCA, and particle swarm optimization (PSO) were applied to the inversion of fundamental and higher-mode dispersion curves, designed under different geological conditions with and without noise contamination, demonstrating the effectiveness and reliability of MSCA in dispersion curve inversion. Finally, the use of measured microtremor dispersion data from Hangzhou, China, further showed that, compared to SCA and PSO, MSCA not only achieved smaller fitting errors and better matching with well-logging data, but also exhibited greater stability, indicating its superiority in surface wave dispersion data inversion and its potential for solving other geophysical inversion problems.

Keywords

• rayleigh wave dispersion
• near-surface
• global optimizer
• benchmark function test