Direct 1D Resistivity Estimation from Data Rescaling Using Cumulative Resistance Models

Abstract

This study presents a novel methodology to transform 1D resistivity data into layered resistivity models without prior information by using the concept of cumulative reference models. The proposed methodology involves deriving an error function that transforms apparent resistance measurements into a cumulative resistance, which is then transformed into a layered resistivity model. We applied the methodology to simulated data from various 1D models with different physical parameters, and the results demonstrate that our method can be used to directly transform the data into a layered resistivity model without requiring prior information. This methodology provides a valuable alternative to inversion methods when one local model is available and multiple measurements are available over an area with similar physical parameters. Furthermore, the retrieved rescaled model can be used as a reference model for the inversion process, reducing computational and economic costs. This study highlights the potential of cumulative reference models for subsurface characterization, providing a new paradigm to study the subsurface with increased efficiency.