基于证据权和随机森林的山东微山稀土矿三维成矿预测
Evidence Right and Random Forest Based 3D Metallogenic Prediction of Weishan REE Deposit in Shandong Province, China
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摘要: 三维矿产资源潜力评价是深部成矿预测的重要手段。近年来，随着机器学习算法的融入，三维矿产资源潜力评价的精准度大幅提高，为矿产资源勘查领域做出了突出贡献。微山稀土矿目前开采标高约-200m，向深部追索探矿效果并不理想，需进一步开展深部找矿勘查工作，本研究利用三维地质建模技术刻画了矿体、岩体以及构造的空间展布形态，并定量提取了岩体、构造、地球化学等有利成矿要素。随后，运用证据权法分析各类地质要素与成矿作用的相关性并计算权重。在此基础上，建立随机森林模型，进行深部成矿预测。结合区域成矿背景，圈定了2处找矿靶区，为郗山稀土矿深部找矿提供了方向。Abstract: 3D mineral resource potential assessment is an important tool for deep ore prediction. In recent years, with the integration of machine learning algorithms, the accuracy of 3D mineral resource potential assessment has significantly improved, making outstanding contributions to the field of mineral resource exploration. The current mining elevation of the Weishan REE deposit is approximately -200m. The exploration results for deep-seated ore prospecting are not satisfactory, and further exploration is needed. In this study, 3D geological modeling was utilized to depict the spatial distribution of ore bodies, rock formations, and structures, and various geological factors were quantitatively extracted. Subsequently, the correlation between various geological factors and mineralization was analyzed using the weight of evidence, Based on this, a random forest model was established for deep ore prediction. Combining with the regional ore-forming background, two exploration targets were identified, providing insights for deep ore exploration in the Weishan REE deposit.
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