Comparative Evaluation of Unsupervised Machine Learning Methods for Orogenic Gold Exploration Using Stream Sediment Geochemistry

Le résumé fourni par la source

Stream sediment geochemistry is a widely used reconnaissance tool in early-stage mineral exploration, particularly in regions where direct evidence of mineralisation is limited. Because stream sediment anomalies provide indirect geochemical signatures and are typically constrained by limited ground-truth information, labelled datasets are often scarce and spatially biased. This limitation restricts the applicability of supervised learning approaches and highlights the need for robust unsupervised methods. In this study, six unsupervised techniques, Principal Component Analysis (PCA), Non-negative Matrix Factorisation (NMF), Uniform Manifold Approximation and Projection (UMAP), Autoencoder (AE), Deep Embedded Clustering (DEC), and an Averaged Ensemble Index (AVE), were evaluated for integrating multivariate stream sediment geochemical data and delineating gold prospectivity zones. Eight gold-related elements (Au, As, Ag, B, Hg, Mo, Sb, and W) were selected based on regional metallogenic characteristics and previously reported geochemical associations. To facilitate direct comparison, all model outputs were normalised to a fuzzy membership scale ranging from 0 to 1. Model performance was quantitatively assessed using Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) and Matthews Correlation Coefficient (MCC) metrics based on independently verified mineralised and non-mineralised locations. The results indicated that DEC and AE consistently outperformed the other methods investigated, achieving the highest ROC–AUC and MCC values, whereas UMAP exhibited comparatively weaker performance. The findings demonstrated that unsupervised representation learning approaches, particularly DEC and AE, provided a more effective framework for integrating multivariate geochemical data and delineating gold-related anomalies in data-limited exploration environments than conventional dimensionality reduction and heuristic integration methods.

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