Abstract
Current machine learning algorithms have achieved remarkable success in quantitatively interpreting spectral gamma‐ray logging for uranium exploration. However, several challenges remain: (1) Existing models typically neglect intervariable dependencies among multiple output variables and (2) they generally lack interpretability. To address these issues, we propose a multitarget regression Takagi–Sugeno–Kang fuzzy model (MTR‐TSK) in this paper. The MTR‐TSK employs fuzzy rules to model feature‐target relationships and jointly optimizes multitarget high‐order correlations through fuzzy inference and regression loss during training. The proposed model not only models the complex relationship between the input and output spaces but also captures and exploits the inter‐variable correlation structure to enhance predictive performance. In addition, the model features a transparent reasoning process, allowing for the identification of feature importance. The model demonstrates strong generalization performance on public benchmark datasets and achieves notable results on our custom uranium mine logging dataset.