基于LightGBM算法的区域成矿预测—以夏河–合作为例

Region Mineralization Prediction Based on the LightGBM Algorithm—A Case Study of Xiahe-Hezuo

摘要: 本研究旨在利用LightGBM算法对夏河–合作地区进行矿产定量预测。LightGBM是一种集成学习方法，是随机森林的一种变体，在构建决策树时采用了随机划分。首先，我们收集了夏河–合作地区的相关地质和地球化学数据。然后，我们对数据进行预处理和特征工程，包括缺失值填充、数据标准化等。接下来，我们构建了LightGBM模型，并对模型进行了训练和调优。最后，我们使用训练好的模型对夏河–合作地区的矿产进行了定量预测，并评估了预测结果的准确性和可靠性。本研究的结果表明，LightGBM算法在夏河–合作地区矿产定量预测中具有较高的效果和潜力，为地质矿产勘查提供了一种有效的预测方法。

Abstract: This study aims to utilize the LightGBM algorithm for quantitative mineral prediction in the Xiahe-Hezuo region. LightGBM, an ensemble learning method and a variant of the random forest, uses random partitioning in decision tree construction. First, we collected relevant geological and geochemical data from the Xiahe-Hezuo region. Then, we performed data preprocessing and feature engineering, including missing value imputation and data normalization. Next, we constructed a LightGBM model and conducted training and tuning. Finally, we used the trained model to make quantitative predictions of mineral resources in the Xiahe-Hezuo region and evaluated the accuracy and reliability of the predictions. The results of this study indicate that the LightGBM algorithm has high effectiveness and potential in quantitative mineral prediction in the Xiahe-Hezuo region, providing an effective prediction method for geological mineral exploration.