Rayleigh surface wave inversion based on an improved Archimedes optimization algorithm

Abstract

Surface wave exploration has the advantages of high shallow resolution, small site limitations, and convenient construction, increasing its use in near-surface exploration. Dispersion curve inversion is an important step in surface wave exploration and is directly related to the acquisition of underground formation information. Similar to numerous geophysical inversion problems, dispersion curve inversion has multiparameter and multiextreme characteristics and is difficult to solve using a linear method. In this paper, a new dispersion curve inversion method based on the Archimedes optimization algorithm (AOA), namely the improved AOA (IAOA), is proposed. IAOA optimizes the population initialization based on AOA and adds the capability to automatically balance global search and local development performance in the iteration process, which enriches the population information in the later stage of AOA iteration. The algorithm is used to invert the noise and noise-free base-step scatter curves of the three theoretical models to test the performance of IAOA for dispersion curve inversion. In the inversion test of the theoretical model of noise-free data, the particle swarm optimization (PSO) algorithm and AOA were also tested in the same inversion test to compare the performances of the PSO, AOA, and IAOA algorithms. The results of the model test revealed that IAOA can stably and quickly converge to the optimal solution, and the inversion results have strong credibility, good noise immunity, and can be effectively used for dispersion curve inversion. Finally, the measured data from the Wyoming area of the United States were utilized to test the capability of IAOA to invert actual data. The inversion results indicated that IAOA has strong practicability and can obtain effective formation information.