Genetic K-Means clustering of soil gas anomalies for high-enthalpy geothermal prospecting: a multivariate approach from southern Tenerife, Canary Islands

High-enthalpy geothermal resources in volcanic settings often lack clear surface manifestations, requiring integrated, data-driven approaches to identify hidden reservoirs. In this study, we apply a multivariate clustering technique-genetic K-Means clustering (GKMC)-to a comprehensive soil gas dataset collected from 1050 sampling sites across the similar to 100 km(2) Garehagua mining license, located in the southern rift zone of Tenerife (Canary Islands). The survey included diffuse CO2 flux measurements and concentrations of key soil gases (He, H-2, CH4, O-2, N-2, Ar isotopes, and Rn-222, among others). Statistical-graphical analysis using the Sinclair method allowed for an objective classification of geochemical anomalies relative to background populations. The GKMC algorithm segmented the dataset into geochemically coherent clusters. One cluster, defined by elevated CO2, helium, and Rn-222 levels, showed a clear spatial correlation with inferred tectonic lineaments in the southern rift zone. These anomalies are interpreted as structurally controlled conduits for the ascent of deep magmatic-hydrothermal fluids. The findings support the presence of a concealed geothermal system structurally constrained in the southern region of Tenerife. This study demonstrates that integrating GKMC clustering with soil gas geochemistry offers a robust methodology for detecting hidden geothermal anomalies. By enhancing anomaly detection in areas with subtle or absent surface expression, this approach contributes to reducing exploration risk and provides a valuable decision-support tool for targeting future drilling operations in volcanic terrains.