Mineral Inversion Constrained by Lithofacies for Prediction of Ga-Rich Laminations in Coal Seams from the Haerwusu Mine, Jungar Coalfield

Gallium (Ga) in coal is a nationally emerging strategic mineral resource, yet research on using petrophysical methods to detect the spatial variation in critical metals in coal seams remains limited. Analyzing the distribution characteristics of Ga-rich coal using geophysical well-logging methods is of great significance for the development and utilization of Ga. This study introduces a quantitative method for predicting Ga-rich laminations in ultra-thick bituminous coal seams by integrating: (i) wireline-log-based lithofacies classification, (ii) lithofacies-constrained mineral inversion, and (iii) lithofacies-constrained and laboratory-established Ga–mineral correlations. The coal seam was first classified into four distinct lithofacies types—(i) parting, (ii) medium-ash coal (MA), (iii) low-ash coal (LA), and (iv) extra-low-ash coal (ELA)—through integration of conventional wireline log interpretation, cluster analysis, and XGBoost machine learning. Second, lithofacies-constrained Ga–host mineral associations were established by integrating core sample analysis, correlation analysis, and linear regression modeling. Third, mineral content predictions for each lithofacies were obtained through wireline-log-based mineral inversion, constrained by petrophysical boundaries. Finally, prediction uncertainties were evaluated using Markov Chain Monte Carlo (MCMC) simulation, while Ga-rich laminations were predicted by integrating log-derived mineral inversion results with regressed Ga prediction models. The results demonstrate strong agreement between mineral inversion and XRD analyses within uncertainty ranges, achieving a prediction accuracy of 73.6% for Ga. This validated methodology presents a novel approach for quantifying Ga concentrations in coal, as demonstrated through a case study.