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A new Automatic Correction scheme for electrical resistivity surveys was developed to address noise contamination. This method uses model response to evaluate data quality, assigns reliability scores, and applies logarithmic averaging. Validation using field data from a riverbank project in Thailand showed the Automatic Correction scheme significantly reduced noise and improved model accuracy compared to standard deviation filtering and despiking.

A new inversion metric, Pattern-Constrained Inversion, was developed to address the limitations of conventional RMS misfit in electrical resistivity surveys. This new metric, tested on synthetic and field data, incorporates data uncertainty and pattern consistency, leading to improved model fidelity and better agreement with borehole observations. The proposed approach outperforms conventional methods, particularly in scenarios with high data uncertainty, providing more accurate resistivity estimates and better structural representation.