Inversion, Assessment of Stability and Uncertainty of Geoelectric Sounding data using a New Hybrid Meta-heuristic algorithm and Posterior Probability Density Function Approach

Abstract. Estimating a reliable subsurface resistivity structure using conventional techniques is challenging due to the nonlinear nature of the inverse problems. The performance of the inversion techniques can be pretty ambiguous based on the optimal error. Although traditional methods have proven to be quite effective. The impact of the constraints accessible from the borehole is examined for further assessment and enhance the algorithm’s effectiveness. The vPSOGWO is a new approach based on model search space without any prior information. This new strategy describes the hybridization of the particle swarm optimizer (PSO) with the grey wolf optimizer (GWO). To understand the efficiency and novelty of the algorithm, it has been validated on two different kinds of synthetic resistivity data with various sets of noise and finally applied on three field datasets of different geological terrains. The analyzed results suggest that the subsurface resistivity model shows considerable uncertainty. Thus, it is superior to examine the histograms and posterior probability density functions (PDF) of such solutions for exemplifying the global solution. PDF with 68.27 % CI selects a region with a higher probability. Therefore, the inverted models are used to estimate the mean global solution and the most negligible uncertainties, where the mean global solution represents the best solution. Our vPSOGWO inverted outcomes have been proven to be more accurate than classic PSO, GWO and state-of-art variant of classic approaches. As a results, this novel method plays a vital role in DC data inversion effectively.