Quasi-2D resistivity model from inversion of vertical electrical sounding (VES) data using guided random search algorithm

Vertical electrical sounding (ves) data are usually interpreted in terms of a 1d resistivity model using linearized inversion. the local approach of a non-linear inverse problem has fundamental limitations,i.e. the necessity of a starting model close to the solution and possible convergence to a local rather than a global minimum solution. we studied the application of a global search approach for non-linear inversion using the guided random search method to model ves data. a quasi-2d resistivity model can be created by stitching 1d models obtained from ves data along a profile. both vertical and lateral resistivity variations are minimized to incorporate a 2d smoothness constraint. the proposed method was applied to invert synthetic ves data as well as field data from a sedimentary environment. both synthetic and field data inversions resulted in models that correlated well with the known synthetic model and with the geology of the study area,respectively. © 2015 published by itb journal publisher.