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

Abstract

Heterogeneous structures and diverse volcanic, hydrothermal, and geomorphological processes hinder 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 dataset characterising the visible-near (VNIR), shortwave (SWIR), midwave (MWIR), and longwave (LWIR) infrared reflectance of samples from eight basaltic to andesitic volcanoes. Several machine learning models were then trained to predict density, porosity, uniaxial compressive strength (UCS), and Young’s modulus (E) from these 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 absorptions that can be attributed to hydrothermal alteration, and MWIR-LWIR features sensitive to volcanic glass content, fabric, and/or surface roughness. These results demonstrate that hyperspectral imaging can serve as a robust proxy for rock physical and mechanical properties, potentially 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.