VFSARES—a very fast simulated annealing FORTRAN program for interpretation of 1-D DC resistivity sounding data from various electrode arrays

Abstract

Employing the very fast simulated annealing (VFSA) global optimization technique, a FORTRAN program is developed for the interpretation of one-dimensional direct current resistivity sounding data from various electrode arrays. The VFSA optimization depicts various good fitting solutions (models) after analyzing a large number of models within a predefined model space. Various models that yield reasonably well fitting responses with the observed response lie along a narrow elongated region of the model space. Therefore, instead of selecting the global model on the basis of the lowest misfit error, it is better to analyze histograms and probability density functions (PDFs) of such models for depicting the global model. In a multidimensional model space, the most appropriate region to select suitable models to compute the mean model is the one in which the PDF is larger in comparison to the other regions of the model space. Initially, accepted models with misfit errors less than the predefined threshold value are selected and lognormal PDFs for each model parameter are computed. Subsequently, mean model and uncertainties are computed using the models in which each model parameter has a PDF more than the defined threshold value (>68.2%). The mean model computed from such models is very close to the actual subsurface structure (global model). It is observed that the mean model computed using models with a PDF more than 95% for each model parameters yields the actual model. Moreover uncertainty computed using models with such a high PDF and lying in a small model space will be small and it will not be considered as the actual global uncertainty. Resistivity sounding (synthetic and field) data over different subsurface structures are optimized using the VFSA program developed in the present study. Optimization results reveal that the actual model always locates within the estimated uncertainty in the mean model. Since the approach requires much less computing time (a few minutes) using an ordinary PC, results with smaller uncertainty can be obtained using repeated computations with a smaller search range in comparison to the results obtained in a large search range. The efficacy of the program is demonstrated by interpreting data from various layered earth structures. Field examples associated with groundwater and mineral exploration are also presented. Interpreted model parameters show excellent correlation with drilling results. The optimization program can be used for various case studies like those associated with groundwater, mineral exploration, subsurface pollution studies, and saline water incursion in coastal areas.