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<p>Mineral resource classification plays a critical role in mine planning and economic valuation, yet it often relies on subjective judgment by a qualified person (QP), leading to inconsistent and non-reproducible block assignments. This study proposes a novel hybrid machine learning framework that integrates unsupervised clustering (<i>K</i>-means for continuous data and <i>K</i>-prototype for mixed data) with supervised smoothing (random forest and XGBoost) to automate and enhance mineral resource classification. Applied to a copper deposit in Peru, the methodology leverages key geostatistical and spatial variables including kriging variance, average sample distance, number of samples, and expert-assigned geological confidence. The best-performing models <i>K</i>-means + XGBoost and <i>K</i>-prototype + XGBoost achieved silhouette scores of 0.11 (compared to 0.03 for the QP baseline), maintained over 95% classification concordance, and reduced spatial inconsistency (transitional misclassifications) by up to 2.1%. Additionally, these models preserved tonnage–grade trends and improved indicator variogram ranges and Moran’s <i>I</i>, reflecting enhanced spatial coherence. This is the first documented application of hybrid clustering-smoothing models to mineral resource classification, offering a reproducible, data-driven, and geologically consistent alternative to manual classification approaches.</p>

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Hybrid Machine Learning Models for Mineral Resource Classification in a Copper Deposit in Peru

  • 期刊论文

  • 发表日期：

    2025年11月24日

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  • Marco A. Cotrina-Teatino,
  • Jairo J. Marquina Araujo

摘要

Mineral resource classification plays a critical role in mine planning and economic valuation, yet it often relies on subjective judgment by a qualified person (QP), leading to inconsistent and non-reproducible block assignments. This study proposes a novel hybrid machine learning framework that integrates unsupervised clustering (K-means for continuous data and K-prototype for mixed data) with supervised smoothing (random forest and XGBoost) to automate and enhance mineral resource classification. Applied to a copper deposit in Peru, the methodology leverages key geostatistical and spatial variables including kriging variance, average sample distance, number of samples, and expert-assigned geological confidence. The best-performing models K-means + XGBoost and K-prototype + XGBoost achieved silhouette scores of 0.11 (compared to 0.03 for the QP baseline), maintained over 95% classification concordance, and reduced spatial inconsistency (transitional misclassifications) by up to 2.1%. Additionally, these models preserved tonnage–grade trends and improved indicator variogram ranges and Moran’s I, reflecting enhanced spatial coherence. This is the first documented application of hybrid clustering-smoothing models to mineral resource classification, offering a reproducible, data-driven, and geologically consistent alternative to manual classification approaches.