Lithology Identification of Uranium-Bearing Sand Bodies Using Logging Data Based on a BP Neural Network

Lithology identification is an essential fact for delineating uranium-bearing sandstone bodies. A new method provided to delineate bodies by a lithological automatic classification model using machine learning techniques, which could also improve the efficiency of borehole core logging. In this contribution, BP neural network lithology was established optimized gradient descent algorithm based on training 4578 sets well logging data (including lithology, density, resistivity, natural gamma, well-diameter, potential, etc.) from 8 boreholes Tarangaole uranium deposit in Inner Mongolia. The softmax activation function and cross-entropy loss are used weight adjustment. prediction carried out 599 samples, with accuracy 88.31%. results suggest that efficient effective, it be directly applied exploration.