Neural network based uncertainty and sensitivity evaluation of electrical resistivity tomography for improved subsurface imaging

Abstract

Assessment of subsurface status by resistivity technique, being an indirect approach, is pretended to be a strategic factor. Projection of full proof confirmation in this domain is always a challenge and hence outcomes are expressed in possibilities. Intervene of mathematical interpretation on resistivity data generated in the field by different arrays would offer a better choice in building-up the possibility of projecting the actual status. Thus, a study of Wenner-Schlumberger (WS), dipole–dipole (DD) and combined inversion (CI) data of three parallel profiles have been conducted, as a whole, for old and abandoned shallow depth coal mine workings in Jharia coalfield. The study recapitulates influence of sensitivity and uncertainty with depth, apart from resistivity. Statistical significance of the data has been evaluated inclusive of their inter-relationship. PCA presented an encouraging relation of sensitivity with depth. The comprehensible approach of mathematical interpretation helps in cracking a problem of uncertain prediction. Sensitivity and the extent of uncertainty are the parameters to build a strong foundation for evaluating the degree of confidence in prediction accuracy. Artificial Neural Network (ANN) tool has been used to understand the relative importance of sensitivity and uncertainty with depth. The weightage of sensitivity has been observed to be on upper side with respect to uncertainty. The importance of configuration of resistivity survey array has been emphasized based on sensitivity.

Keywords

• Resistivity
• Sensitivity
• Uncertainty
• Wenner-Schlumberger
• Dipole-dipole
• Combined inversion