Abstract:

Three-dimensional metallogenic prediction overcomes the limitations of traditional two-dimensional prediction maps and can represent geo-information directly in true 3D space, so it has attracted more and more attention. 3D geological modeling and metallogenic prediction methods are important steps in the process of 3D metallogenic prediction. However, with the diversification of geological data collection methods and the diversity of geological data sources, geological data has gradually acquired the characteristics of big data. Traditional explicit modeling methods and extraction methods of favorable metallogenic information face many limitations. These limitations are mainly reflected in the inability to effectively update the 3D model in real time and analyze a large number of geological survey data. To address these challenges, the author selects the Julong super-large porphyry copper-molybdenum deposit in Tibet as the research object, employs implicit modeling methods to build a 3D geological-geochemical model of the mining area, and uses the machine learning algorithm to extract and analyze favorable metallogenic information, and finally predicts the potential favorable metallogenic space. The modeling results show that the implicit modeling method can obtain the geological entities of the whole space through the interpolation function, and the 3D geological body surface reconstruction algorithm can model and visualize the geological entities and automatically generate the 3D visualization model. Implicit modeling greatly reduces the cumbersome process of human-computer interaction delineating geological boundaries in explicit modeling, and can realize rapid dynamic update of 3D models. Moreover, implicit modeling can accurately reflect the spatial distribution characteristics of deep underground geological bodies to a greater extent. Metallogenic prediction is mainly based on 3D geological and geochemical models to extract deep favorable metallogenic information, build training sets and test sets, and train supervised machine learning models (logistic regression model, support vector machine model, artificial neural network model, random forest model) respectively. The trained model makes predictions to the test set, and plots the prediction results on Receiver Operating Characteristic (ROC) curves to evaluate the prediction results. The evaluation results show that the Area Under the Curve (AUC) values of the four different prediction models are all greater than 0.6, indicating that the prediction accuracy of the trained model is better than that of the random process, among which the AUC value of the random forest algorithm is the largest (0.97), and the model has the best prediction effect. In this paper, the random forest model is selected to predict the depth extent of the mineralization, and two prospecting targets are delineated. After drilling verification, the potential resources in target area B are consistent with the predicted results, which proves that the method is scientific and feasible.

Key words: Julong copper-molybdenum deposit, implicit modeling, machine learning algorithm, metallogenic prediction