Resource potential evaluation of magmatic cobalt and nickel in the east Kunlun metallogenic belt, northwest of China through a geological-constrained convolutional neural network model

In the Kunlun orogenic belt of the East Kunlun region, several magmatic sulfide deposits of cobalt–nickel type, such as Xiarihamu and Shitoukengde, have been discovered along the Kunlun fault. The area still holds significant mineral exploration potential. This paper proposes a convolutional neural network (CNN) model based on geological constraints. When constructing the CNN model, a layer is added to restrict the magnesium-iron ratio content of mafic–ultramafic rocks in the convolutional layer, based on the characteristics of mafic–ultramafic rocks in cobalt–nickel deposits. This layer acts as a hard constraint to filter mafic–ultramafic rocks in the evidence layer, providing theoretical interpretability to the data-driven CNN model. To ensure sample balance in the prediction data and prevent overfitting during the model prediction process, a sliding window method is adopted to expand the positive samples. Predictive models are established before and after sample expansion, and ROC is used to evaluate the predictive models. Based on research into the metallogenic geological background and metallogenic model of the East Kunlun Orogenic Belt, combined with multi-source geological, geophysical, and geochemical data, a geological-constrained CNN model was used to analyze the mineralization potential of magmatic cobalt–nickel deposits in the study area. The research results show that the accuracy of the geology-constrained CNN model is 92.1 %, indicating excellent predictive performance. The results, highly consistent with known deposits, suggest that the model’s predictions can be used for resource potential assessment of magmatic cobalt–nickel deposits in the East Kunlun metallogenic belt in northwest China.