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Abstract <p>The study presents a methodological approach to predictive modeling of gold–sulfide mineralization zones in Northern and Central Chukotka using multisensor and multitemporal satellite remote sensing data (Landsat-8, Sentinel-2, ASTER). Spectral analysis methods, including BR, RBD, PCA, and SPCA, were applied to identify hydrothermal–metasomatic alteration. Statistical analysis enabled the selection of thematic layers indicating zones of argillic alteration, phyllic (sericitic) alteration, propylitic alteration, iron oxides/hydroxides, and silicification. Integration of these layers using a fuzzy logic model made it possible to construct a predictive exploration scheme, identify anomalies associated with known ore occurrences and controlling structures, and delineate new, potentially promising areas for gold–sulfide mineralization.</p>

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Predictive Modeling of Gold–Sulfide Mineralization Zones Using Satellite Multispectral Data (Northern and Central Chukotka)

  • 期刊论文

  • 发表日期：

    2026年2月1日

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  • I. O. Nafigin,
  • D. S. Lapaev,
  • V. A. Minaev,
  • S. A. Ustinov,
  • V. A. Petrov

摘要

Abstract

The study presents a methodological approach to predictive modeling of gold–sulfide mineralization zones in Northern and Central Chukotka using multisensor and multitemporal satellite remote sensing data (Landsat-8, Sentinel-2, ASTER). Spectral analysis methods, including BR, RBD, PCA, and SPCA, were applied to identify hydrothermal–metasomatic alteration. Statistical analysis enabled the selection of thematic layers indicating zones of argillic alteration, phyllic (sericitic) alteration, propylitic alteration, iron oxides/hydroxides, and silicification. Integration of these layers using a fuzzy logic model made it possible to construct a predictive exploration scheme, identify anomalies associated with known ore occurrences and controlling structures, and delineate new, potentially promising areas for gold–sulfide mineralization.