Multidirectional Ultrasound Propagation Velocity as a Predictor of Open Porosity and Water Absorption in Volcanic Rocks: Traditional Regression and Machine Learning

Ultrasound propagation velocity was investigated as a non-destructive predictor of open porosity (ρ0) and water absorption (Aw) in volcanic rocks (two ignimbrites, a trachyte, and a basalt). Six velocity measurements were obtained under dry and saturated conditions along three orthogonal directions, and the dry Z-axis velocity was selected as the reference univariate predictor because it provided the highest explanatory power and the best cross-validated performance among the tested ultrasound variables. Four univariate regressions (linear, exponential, power law, and second-order polynomial), parametric multivariable linear regression, and five machine learning regressors were compared using lithology-stratified 5-fold cross-validation, grouping both ignimbrites as a single lithology. Univariate models showed moderate predictive capability for ρ0 (cross-validated coefficient of determination R2≈ 0.506 to 0.580), whereas Aw was captured more accurately, with the power law model reaching 0.923 ± 0.008. Multivariable linear regression improved ρ0 when lithology was included (0.803 ± 0.084), while changes for Aw were small. The highest accuracy was achieved by ensemble tree methods: extremely randomized trees with lithology yielded 0.949 ± 0.015 for ρ0 (root mean square error 2.16 ± 0.38 percentage points), and Gradient Boosting with lithology yielded 0.976 ± 0.006 for Aw (0.80 ± 0.12 percentage points).