Towards electrical resistivity tomography interferometry: Leveraging spatial redundancy with machine learning and information theory

Electrical resistivity tomography (ERT) surveys produce multilevel apparent resistivity measurements whose internal redundancy is rarely quantified. Using 5 years of rolling Wenner‐β acquisition in Ireland, we construct a national‐scale database. Building on it, we use an information‐theoretic framework to estimate the proportion of measurable values that should, in principle, be inferable from neighbouring levels. To test these predictions, we implement a unified machine‐learning pipeline, PCA‐based preprocessing followed by supervised regression, and, in parallel, attempt a Monte Carlo data‐augmentation scheme. The latter is obtained by fitting a three‐component Gaussian mixture model using expectation–maximization to model the joint distribution of inputs and log‐transformed targets, from which synthetic samples are generated to stabilize learning in sparsely sampled resistivity ranges. Seven algorithms (Neural Network, Random Forest, Extra Trees, XGBoost, CatBoost, SVM and Decision Tree) consistently achieved a relative MAE below 2%. Across these models, 70%–80% of apparent resistivity values on an independent survey were inferred within 5% error, closely matching the information‐theory expectations on redundancy. These results demonstrate that multilevel ERT contains substantial predictable structure governed by regional geology, opening a pathway towards targeted acquisition‐time reduction and more efficient near‐surface characterization.