Simple linear regression (SLR) models for rapid estimation of true subsurface resistivity from apparent resistivity measurements are developed and assessed in this study. The objective is to minimize the processing time and computer memory required to carry out inversion with conventional algorithms. The arrays considered are Wenner, Wenner–Schlumberger and dipole–dipole. The parameters investigated are apparent resistivity (ρa\documentclass[12pt]{minimal}
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\begin{document}$$\rho _a $$\end{document}) and true resistivity (ρt\documentclass[12pt]{minimal}
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\begin{document}$$\rho _t$$\end{document}) as independent and dependent variables, respectively. For the fact that subsurface resistivity is nonlinear, the datasets were first transformed into logarithmic scale to satisfy the basic regression assumptions. Three models, one each for the three array types, are thus developed based on simple linear relationships between the dependent and independent variables. The generated SLR coefficients were used to estimate ρt\documentclass[12pt]{minimal}
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\begin{document}$$\rho _t$$\end{document} for different ρa\documentclass[12pt]{minimal}
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\begin{document}$$\rho _a$$\end{document} datasets for validation. Accuracy of the models was assessed using coefficient of determination (R2)\documentclass[12pt]{minimal}
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\begin{document}$$R^{2})$$\end{document}, F-test, standard error (SE) and weighted mean absolute percentage error (wMAPE). The model calibration R2\documentclass[12pt]{minimal}
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\begin{document}$$R^{2}$$\end{document} and F-value are obtained as 0.75 and 2286, 0.63 and 1097, and 0.47 and 446 for the Wenner, Wenner–Schlumberger and dipole–dipole array models, respectively. The SE for calibration and validation are obtained as 0.12 and 0.13, 0.16 and 0.25, and 0.21 and 0.24 for the Wenner, Wenner–Schlumberger and dipole–dipole array models, respectively. Similarly, the wMAPE for calibration and validation are estimated as 3.27 and 3.49%, 3.88 and 5.72%, and 5.35 and 6.07% for the three array models, respectively. When compared with standard constraint least-squares (SCLS) inversion and Incomplete Gauss–Newton (IGN) algorithms, the SLR models were found to reduce about 80–96.5% of the processing time and memory space required to carry out the inversion with the SCLS algorithm. It is concluded that the SLR models can rapidly estimate ρt\documentclass[12pt]{minimal}
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\begin{document}$$\rho _t$$\end{document} for the various arrays accurately.
