关键词: 机器学习, 多源数据, 岩性识别, 随机森林, 人工神经网络

Abstract:

The Eastern Tianshan region is an important metallogenic belt and exhibits a complex tectonic evolution, with extensive exposures of intermediate-acidic intrusive rocks primarily formed during the Late Paleozoic. Understanding their relationship with regional tectonic evolution and the formation of magmatic-hydrothermal associated metal deposits is of great significance for comprehending the regional tectonic environment and ore-forming patterns. However, the covering layers have resulted in incomplete geological mapping of the intermediate-acidic intrusive rocks in the covered areas of the Eastern Tianshan region. This has hindered our understanding of the regional tectonics and ore-forming patterns there. In recent years, a new paradigm has emerged that integrates multisource survey data, such as geophysics, geochemistry, and remote sensing imagery, using big data analytical techniques to support lithological mapping. Machine learning algorithms have been demonstrated to be powerful tools for data fusion, making them applicable to problems involving the classification and discrimination of complex nonlinear geological data. Therefore, this study proposes using machine learning methods to integrate gravity, aeromagnetic, geochemistry, and remote sensing imagery data to conduct rapid, cost-effective, and more accurate lithological mapping of intermediate-acidic intrusive rocks in the Eastern Tianshan district. In this contribution, the exposed intermediate-acidic intrusive rocks of the study area are labeled as target variables. Furthermore, as predictive variables, Bouguer gravity, aeromagnetic, stream sediment geochemical, and Landsat satellite imagery data are employed. Synthetic minority oversampling technique is utilized to address the issue of imbalanced lithological sample data distribution. Random forest (RF) and artificial neural network (ANN) algorithms are applied, and hyperparameter tuning is conducted through grid search to obtain the optimal prediction models. These models are then used to identify concealed intermediate-acidic intrusive rocks in the covered areas of the Eastern Tianshan region. The results of RF are compared and analyzed with those of ANN. Accuracy, recall rate, and F1 scores indicate that the RF model outperforms the ANN model. Therefore, the prediction results of the RF model are selected as the final result for lithological mapping of intermediate-acidic intrusive rocks in the covered areas of the Eastern Tianshan region. Further discussions are conducted on the control patterns of the spatial distribution of intermediate-acidic intrusive rocks on regional tectonics and mineralization. Compared to traditional geological mapping methods, the machine learning-based lithological mapping approach, which integrates multiple data sources, offers advantages including increased depth, high recognition efficiency, and lower costs, making it an effective method for comprehensively exploring potential geological features and patterns.

Key words: machine learning, multisource data, lithological identification, random forest, artificial neural network