Data-Driven Resistivity Zonation: Integrating Inversion, Kriging, and Clustering to Unravel Subsurface Complexity for Groundwater Exploration

This study integrates geoelectrical resistivity inversion, geostatistical analysis, and clustering techniques to improve subsurface characterization in Cikole, Lembang, West Bandung Regency. The primary objective is to enhance resistivity-based zonation for groundwater exploration by employing a dipole-dipole array with 10-meter electrode spacing, followed by 2D inversion to visualize resistivity distributions. Geostatistical interpolation using Kriging effectively captures spatial variability, while clustering methods (K-Means, DBSCAN, and Hierarchical Clustering) classify resistivity-based subsurface zones. The results reveal that low-resistivity zones (< 30 Ωm) correspond to water-bearing formations, whereas high-resistivity regions (> 100 Ωm) are associated with compacted volcanic deposits. Among the clustering methods, Hierarchical Clustering provides the most geologically meaningful classification by preserving gradual resistivity transitions. The integration of clustering with geostatistical interpolation enhances subsurface interpretation, reducing uncertainty in hydrogeological assessments. However, DBSCAN's sensitivity to parameter selection limits its effectiveness in identifying multiple resistivity clusters. This study confirms that the combination of geoelectrical inversion, geostatistics, and clustering improves the accuracy of subsurface mapping, offering a data-driven approach applicable to complex geological environments. Future research should focus on optimizing DBSCAN parameters and incorporating additional geophysical methods to refine groundwater characterization.