Study on quantitative interpretation of uranium spectral gamma-ray logging based on machine learning algorithm

Abstract

Spectral gamma-ray logging stands out as an efficacious methodology in the exploration of uranium(U). In order to address the challenges associated with low statistical efficacy in spectral analysis and mitigate the impact of spectral drift, machine learning algorithms such as Back Propagation (BP) neural network, Generalized Regression Neural Network (GRNN), and Support Vector Machine (SVM) have been employed for the quantitative interpretation of uranium. A method rooted in machine learning algorithms for energy spectral analysis has been proposed, catering to the analysis of high-speed spectra logging and gamma-ray spectra characterized by spectral drift. Examinations conducted on standard model wells containing uranium revealed that the BP neural network exhibited commendable accuracy in uranium interpretation, achieving quantification accuracy rates of 86.986 % and 93.478 % for low-grade and medium-high-grade uranium ores in the Testing Set, respectively. Notably, marginal variations in the model's quantification errors were observed under diverse logging speeds and spectral drift conditions, underscoring the capacity of gamma-ray spectral quantitative interpretation through machine learning algorithms to effectively surmount the impacts of logging speed and spectral drift. This machine learning-based approach to energy spectral analysis proved instrumental in enhancing traditional logging speeds to 6 m/min, presenting a novel perspective for the quantitative interpretation of uranium spectral gamma-ray logging at heightened logging speeds.