Author:
Hu Benna,Wen Laifu,Zhou Xuemei
Abstract
Purpose
Vertical electrical sounding (VES) and Rayleigh wave exploration are widely used in the exploration of near-surface structure, but both have limitations. This study aims to make full use of the advantages of the two methods, reduce the multiple solutions of single inversion and improve the accuracy of the inversion. Thus, a nonlinear joint inversion method of VES and Rayleigh wave exploration based on improved differential evolution (DE) algorithm was proposed.
Design/methodology/approach
Based on the DE algorithm, a new initialization strategy was proposed. Then, taking AK-type with high-velocity interlayer model and HA-type with low-velocity interlayer model near the surface as examples, the inversion results of different methods were compared and analyzed. Then, the proposed method was applied to the field data in Chengde, Hebei Province, China. The stratum structure was accurately depicted and verified by drilling.
Findings
The synthetic data and field data results showed that the joint inversion of VES and Rayleigh wave data based on the improved DE algorithm can effectively improve the interpretation accuracy of the single-method inversion and had strong stability and large generalizable ability in near-surface engineering problems.
Originality/value
A joint inversion method of VES and Rayleigh wave data based on improved DE algorithm is proposed, which can improve the accuracy of single-method inversion.
Subject
Electrical and Electronic Engineering,Mechanical Engineering,Mechanics of Materials,Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering
Reference41 articles.
1. Population set-based global optimization algorithms: some modifications and numerical studies;Computers & Operations Research,2004
2. An implementation of differential evolution algorithm for inversion of geoelectrical data;Journal of Applied Geophysics,2013
3. 3D non-linear inversion of magnetic anomalies caused by prismatic bodies using differential evolution algorithm;Journal of Applied Geophysics,2016
4. An adaptive regularized inversion algorithm for magnetotelluric data;Chinese Journal of Geophysics,2005
5. Deep learning inversion of Rayleigh-wave dispersion curves with geological constraints for near-surface investigations;Geophysical Journal International,2022
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献