Estimation of inverted electrical resistivity and advanced modelling of iron ore using machine learning models based on geophysical array data

Accurate estimation of inverted electrical resistivity values is essential for reliable geophysical exploration, particularly in identifying mineral deposits such as iron ore. Traditional geoelectrical methods, including combined resistivity profiling, gradient, pole–dipole and Schlumberger arrays, are valuable but face challenges such as data interpretation difficulties at unmeasured locations, noise sensitivity and high survey costs. This study applies advanced machine learning (ML) techniques to simultaneously improve the precision and spatial continuity of inverted resistivity estimation and modelling of iron ore deposits. The improved resistivity maps enable more reliable delineation of magnetite‐rich zones and clearer discrimination of lithological units, thus supporting 3D ore‐body visualization and more targeted drilling and resource evaluation. Consequently, the results have direct implications for exploration efficiency and mine planning by reducing uncertainty in subsurface interpretations. ML models such as random forests (RFs), categorical boost and decision trees are employed to analyse geoelectrical data, with a focus on overcoming issues related to outliers. The generalized extreme studentized deviate (GESD) test is used to identify and remove outliers, and hyperparameters are optimized through grid search and cross‐validation to improve model performance. A dataset of 9562 data points from multiple array configurations is used, and model efficacy is evaluated using metrics including root mean square error (RMSE), standard deviation, Nash–Sutcliffe efficiency, mean absolute percentage error and r . Results indicate that the RF model, particularly when trained on GESD‐processed data and optimized through grid search and cross‐validation, delivers the most accurate inverted resistivity estimates. The RF model achieved an RMSE of 7.23 Ω m and a Pearson correlation coefficient ( R ) of 0.94, highlighting its robustness. The study demonstrates that integrating ML with geoelectrical data provides a robust framework for extending inverted resistivity coverage in unsampled zones, offering a more efficient and cost‐effective approach compared to traditional methods. This research highlights the potential of combining conventional geophysical techniques with advanced data science to enhance subsurface exploration and resource evaluation.