Data-Driven Hydrothermal Gold Prospectivity Mapping in Western Java Using Integrated GIS Techniques

Arie Naftali Hawu Hede (a*), Sarah Humaira (b), Mohamad Nur Heriawan (a)

Data-Driven Hydrothermal Gold Prospectivity Mapping in Western Java Using Integrated GIS Techniques
Arie Naftali Hawu Hede (a*), Sarah Humaira (b), Mohamad Nur Heriawan (a)

a) Earth Resources Exploration Research Group, Faculty of Mining and Petroleum Engineering, Institut
Teknologi Bandung, Jalan Ganesha 10, Bandung, Indonesia 40132
*ariehede[at]itb.ac.id
b) Undergraduate Program of Mining Engineering, Faculty of Mining and Petroleum Engineering, Institut
Teknologi Bandung, Jalan Ganesha 10, Bandung, Indonesia 40132

Abstract

The western part of Java Island, Indonesia, is a region with a rich geological history and significant potential for gold mineralization. However, the island remains under-explored due to its dense vegetation cover and rugged terrain, hindering traditional exploration efforts. This study employs an integrated geographic information system (GIS) and remote sensing approach to overcome these challenges and delineate prospective targets for hydrothermal gold mineralization in western Java. We utilize a comprehensive dataset encompassing surface geochemical surveys, detailed geological maps, multispectral satellite imagery, and geophysical data. Advanced spectral analysis of satellite imagery identifies and maps the spatial distribution of key alteration mineral assemblages, such as argillic and propylitic alteration, known to be associated with epithermal gold deposits. Surface geochemical data are analyzed to identify and delineate spatial patterns of pathfinder element anomalies which indicate hydrothermal fluid activity. Structural interpretation of geological maps, combined with geophysical data, highlights fault systems, lithological contacts, and other structural features that act as conduits for gold-bearing fluids and potential traps for mineralization. Machine learning algorithms, trained on the spatial relationships between these evidential layers and locations of known gold deposits, generate a predictive mineral prospectivity map for the region. The resulting map classifies the study area into zones of high, moderate, and low prospectivity for hydrothermal gold mineralization, providing a valuable tool for prioritizing exploration efforts and guiding further detailed geological investigations.

Keywords: gold mineralization- GIS- remote sensing- predictive mapping- machine learning

Topic: Hydrology, hydrogeology, and geology engineering