Quasi-2D Resistivity Model from Inversion of Vertical Electrical Sounding (VES) Data using Guided Random Search Algorithm

Abstract

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.

Keywords

• 2D model
• DC resistivity
• global search
• Monte Carlo method
• non-linear inversion