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Abstract

Self-Potential (SP) is one of the oldest geophysical method that have been widely used in numerous applications. Despite its easy measurement techniques, the interpretation of SP data is quite challenging due to the complexities in the source mechanisms. Therefore, various local and global geophysical inversion approaches were developed and tested on inverting the SP data for estimating the source parameters. However, the inherent issue of uncertainties in the model parameters was tackled in a very few studies, mostly using the optimization or deterministic methods by collecting of models iteratively. The main disadvantage of such methods is the biased sampling even starting from a random initial model. On the other hand, algorithms based on probabilistic approaches samples with biasness using the statistical distribution and map the model space effectively. In this work, we use an inversion approach based on the Markov Chain Monte Carlo (MCMC) method. Uncertainties quantification is performed on the posterior distribution using the statistical methods. At first, we invert the SP data generated due to single and multiple simple shaped polarized structures and a detailed analysis on uncertainty associated with the interpretation was carried out. The efficacy of the method was demonstrated using noise free and noisy synthetic examples. After successful application on synthetic examples, we then apply it on the real field examples.