Intelligent Outlier Reconstruction for Enhancing Fractal Anomaly Mapping: A Machine Learning-Based Approach to Explore Shear Zone Gold Deposits

Geochemical gold datasets from shear zone-hosted systems frequently contain extreme outliers that distort statistical structure, shift population boundaries, and undermine the reliability of concentration–area (C–A) fractal modeling. Conventional treatments such as discarding anomalous samples or applying fixed Winsorization thresholds often fail to preserve the multivariate relationships that control geochemical dispersion. In this research, an intelligent random forest (RF)-based model was developed to reconstruct an extreme Au outlier in stream sediment samples from the Saqqez shear zone belt by leveraging available multielement geochemical information. This study introduces a hybrid correction framework based on a machine learning algorithm and targeted Winsorization (MLA–TW) that integrates TW with RF regression to reconstruct a realistic and geochemically plausible value for a highly influential Au outlier. Three scenarios were examined: (1) modeling with the original dataset containing a 739 ppb outlier, (2) modeling after removing the outlier, and (3) modeling with a reconstructed value obtained from the MLA–TW approach. The RF model showed reliable predictive capacity (R2 = 0.85), and the reconstructed value preserved both geological plausibility and nonlinear multivariate structure. Application of the C–A fractal model demonstrated that the MLA–TW scenario yielded the most stable population breaks, the most robust anomaly thresholds, and the highest spatial fidelity, successfully identifying verified gold prospects and deposits in the region. Overall, the MLA–TW framework stabilizes the C–A model and improves its robustness by reducing the statistical leverage of extreme values while preserving the nonlinear geochemical patterns essential for anomaly detection. The results confirm that this intelligent hybrid approach provides an objective and geologically meaningful methodology for refining Au threshold determination and delineating shear zone-related gold targets with improved accuracy.