Predicting petrophysical properties from hyperspectral borehole data

Recent research has highlighted significant correlations between hyperspectral data and the petrophysical properties of geological formations. Petrophysics acts as the link between geology and geophysics, and is crucial for constraining geophysical inversions, regional characterisation and mineral exploration. In this study, we employ various machine learning methods to predict P-Wave velocities using hyperspectral borehole data, with a focus on cross-validation between different boreholes in the same region. Our dataset includes 4022 paired observations of P-wave velocities, obtained from downhole sonic logging in one borehole, and corresponding hyperspectral data spanning visible-near (380–970 nm), shortwave (970–2500 nm), midwave (2700–5300 nm), and long-wave (7700–12300 nm) spectra, averaged over a 10 cm x 5 cm area. We utilised principal component analysis (PCA) for dimensionality reduction. The initial PCA stage extracted 10 principal components from each sensor type, which were then integrated. A subsequent PCA stage was conducted to reduce inter-sensor correlation, yielding 10 composite features that represent the variability across the complete VNIR-LWIR spectrum. To validate our model, we conducted tests using 1160 pairs of analogous measurements from a different borehole within the same geological region. The model demonstrated impressive predictive capabilities, particularly with Support Vector Regression (SVR) and Artificial Neural Networks (ANN). The test set yielded R2 scores of 0.758 for SVR and 0.811 for ANN, indicating strong predictive accuracy. Building upon this success, our future work will expand the scope of prediction to include various other petrophysical properties critical to geophysical characterization and mineral exploration, such as S-Wave velocity, magnetic susceptibility, and rock density, properties which are critical for geophysical characterization and mineral exploration.