Predicting Mechanical Properties of Carbonate Rocks Using Spectroscopy Across 0.4–12 μm

Determining the mechanical characteristics of rocks is crucial in various civil engineering sectors. Traditionally, the mechanical properties of rocks are determined through on-site and laboratory tests carried out during geotechnical surveys. However, these extensive surveys require considerable time and resources. In contrast, hyperspectral remote sensing techniques offer a rapid and simple means to determine the mineral composition and crystallographic structure of rocks. These features, in turn, influence the rocks' mechanical properties. This study focuses on characterizing the mechanical properties of carbonate rocks in a laboratory setting, using hyperspectral sensors. Approximately 150 cylindrical carbonate rock samples, spanning a wide strength range, were collected from diverse Israeli rock outcrops. Employing a point spectrometer (0.4 to 2.5 µm) and a spectral image sensor (8.0 to 12.0 µm), we captured samples' light reflections and spectral emissivity. Mechanical attributes, including density, porosity, water absorption, and uniaxial compressive strength (UCS), were measured. Advanced data mining techniques identified statistical correlations between hyperspectral signatures and mechanical properties, pinpointing key wavelengths for prediction. The developed models exhibited excellent predictability for the specified properties, attributing accuracy to discernible mineralogy and internal crystalline structure through spectroscopy. However, predicting UCS showed slightly weaker results due to influences from internal flaws not entirely reflected in spectroscopic data. Nonetheless, outcomes regarding rock UCS were deemed satisfactory. These findings open avenues for non-destructive tools in assessing the mechanical properties of rocks in quarrying operations. Highlights We developed a new method for evaluating the mechanical properties of carbonate rocks using non-destructive spectroscopy. We applied sophisticated data mining techniques to identify statistical correlations between the hyperspectral signatures and mechanical properties of rock samples. We found the key wavelengths for predicting density, porosity, water absorption, and uniaxial compressive strength of the rock samples. The ability to assess the mechanical properties of intact rocks through remote sensing can improve the fieldwork of an engineering geologist.