Comparative Machine Learning-Based Prediction of Gold Enrichment in a Sulphide-Hosted Orogenic System Using Multielement Geochemistry

Accurate prediction of gold enrichment is critical for mineral exploration and resource evaluation, particularly in data-limited environments where only geochemical information is available. This study evaluates machine learning (ML) models against linear baselines for predicting relative gold enrichment defined as ln(Au/G10), where G10 represents the geometric mean of ten predictor elements (S, Fe, Al, Si, Mn, Sr, Ni, Cu, K, Ti). A total of 53,126 samples from a sulphide-hosted orogenic gold sy