Hyperspectral mapping of density, porosity, stiffness, and strength in hydrothermally altered volcanic rocks

Abstract. Heterogeneous structures and diverse volcanic, hydrothermal, and geomorphological processes hinder the characterisation of the mechanical properties of volcanic rock masses. Laboratory experiments can provide accurate rock property measurements, but are limited by sample scale and labor-intensive procedures. In this contribution, we expand on previous research linking the hyperspectral fingerprints of rocks to their physical and mechanical properties. We acquired a unique reflectance dataset covering the visible-near infrared (VNIR), shortwave infrared (SWIR), midwave infrared (MWIR), and longwave infrared (LWIR) of rocks sampled on eight basaltic to andesitic volcanoes. We trained several machine learning models to predict density, porosity, uniaxial compressive strength (UCS), and Young's modulus (E) from the spectral data. Significantly, nonlinear techniques such as multilayer perceptron (MLP) models were able to explain up to 80 % of the variance in density and porosity, and 65–70 % of the variance in UCS and E. Shapley value analysis, a tool from explainable AI, highlights the dominant contribution of VNIR-SWIR features that can be attributed to hydrothermal alteration and MWIR-LWIR features witnessing volcanic glass content and, likely, fabric and/or surface roughness. These results demonstrate that hyperspectral imaging can serve as a robust proxy for rock physical and mechanical properties, offering an efficient, scalable method for characterising large areas of exposed volcanic rock. The integration of these data with geomechanical models could enhance hazard assessment, infrastructure development, and resource utilisation in volcanic regions.