Accuracy of the models was assessed using coefficient of determination ( \(R^\) storage site, Ketzin, Germany Geophys. The generated SLR coefficients were used to estimate \(\rho _t\) for different \(\rho _a\) datasets for validation. Three models, one each for the three array types, are thus developed based on simple linear relationships between the dependent and independent variables. For the fact that subsurface resistivity is nonlinear, the datasets were first transformed into logarithmic scale to satisfy the basic regression assumptions.
The parameters investigated are apparent resistivity ( \(\rho _a \)) and true resistivity ( \(\rho _t\)) as independent and dependent variables, respectively. The arrays considered are Wenner, Wenner–Schlumberger and dipole–dipole. The objective is to minimize the processing time and computer memory required to carry out inversion with conventional algorithms. Simple linear regression (SLR) models for rapid estimation of true subsurface resistivity from apparent resistivity measurements are developed and assessed in this study.