Lithology identification is an essential fact for delineating uranium-bearing sandstone bodies. A new method is provided to delineate sandstone bodies by a lithological automatic classification model using machine learning techniques, which could also improve the efficiency of borehole core logging. In this contribution, the BP neural network model for automatic lithology identification was established using an optimized gradient descent algorithm based on the neural network training of 4578 sets of well logging data (including lithology, density, resistivity, natural gamma, well-diameter, natural potential, etc.) from 8 boreholes of the Tarangaole uranium deposit in Inner Mongolia. The softmax activation function and the cross-entropy loss function are used for lithology classification and weight adjustment. The lithology identification prediction was carried out for 599 samples, with a prediction accuracy of 88.31%. The prediction results suggest that the model is efficient and effective, and that it could be directly applied for automatic lithology identification in sandstone bodies for uranium exploration.
It is common that sandstone-type uranium deposits co-exist with coal and oil & gas in the same sedimentary basin, and the output of uranium deposits is closely related to the coal seam or oil & gas reservoir. An “upper uranium and lower coal” ore-bearing pattern is formed in Ordos Basin and Erlian Basin in China. Due to the absence of unified resource mining plan and policy coordination mechanism, the coal-uranium resource mining contradictions have become increasingly prominent in coal-uranium coexisting areas in recent years. It is difficult to realize in-situ leaching exploitation of the coal-uranium resources in the Ordos coal-uranium coexisting area because of disorderly mineral resource mining, radioactive contamination risks are posed to the ambient environment of the mining area, and consequently, coal mine enterprises have to stop work and production, which gives rise to huge economic loss. Given this, the current resource contradictions in this area and their influence paths were comprehensively expounded through a systematic survey on a coal-uranium superposition area, and then the corresponding countermeasure suggestions were proposed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.