Co-Operative Inversion of Multi-Channel Analysis of Surface Waves and Electrical Resistivity Tomography Using Fuzzy Clustering Approach

Abstract

Summary Seismic and electrical resistivity tomography methods are recognized as powerful tools to investigate landslides. The effectiveness of the investigation significantly increases if we can exploit the strength of the each method and complement this information in a combinative model. In this study, we performed co-operative inversion of two datasets (multi-channel analysis of surface waves and electrical resistivity tomography) via a fuzzy clustering technique. Our results demonstrate the robustness of our routine. In our single data inversion process, this method can construct models that include zones relating similar parameters. A conventional inversion process uses smoothing criteria to find a final solution, but this approach cannot image a subsurface with highly variable physical properties due to faults and groundwater seeps present in our survey area. The co-operative inversion of the seismic and geo-electrical data exploits the advantages of the both methods, to build a better model than models inverted from the single datasets. Our inversion model provides a more reasonable subsurface model than conventional inversion results. Finally, our process can provide the clustering image, namely pseudo-lithology, from the co-operative inversion models of the two methods. This makes interpretation much easier than using the inverted models.