A method for mineral prospectivity mapping integrating C4.5 decision tree, weights-of-evidence and m-branch smoothing techniques: a case study in the eastern Kunlun Mountains, China
A method for mineral prospectivity mapping integrating C4.5 decision tree, weights-of-evidence and m-branch smoothing techniques: a case study in the eastern Kunlun Mountains, China
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DOI：
10.1007/s12145-013-0128-0
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发表时间：
2014-03
影响因子：
2.8
通讯作者：
Cuihua Chen;B. He;Z. Zeng
中科院分区：
文献类型：
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作者：
Cuihua Chen;B. He;Z. Zeng
在这项研究中，提出了一种集成C4.5决策树、证据权重和m分支平滑技术的矿产勘查制图方法。首先，使用证据权重模型对每个证据地图的重要性进行排序，并确定最优缓冲距离。其次，利用数据挖掘中的C4.5决策树分类技术，为网格数据集构造决策树分类器。最后，使用m-分支平滑技术作为预测器，将决策树转化为概率评估树。该方法不作条件独立性假设，适用于矿产勘查中采集的类不平衡数据集。从C4.5决策树中获得了可理解性、准确性和高效性的特征。此外，还在东昆仑山地区进行了找矿找矿实例研究，即中国。研究了62个矽卡岩型铁矿和8个与铁矿化有关的证据图。在最终地图上，低、中、高铁矿赋存潜力区面积分别为71,491，14,298和9,532平方公里。适合度检验，62个铁矿中，91.94%位于高势区，8.06%位于中势区，0%位于低势区。对于10倍交叉验证，82.26%的样本位于高势区，14.52%的样本位于中势区，3.22%的样本位于低势区。采用受试者工作特征(ROC)曲线和ROC曲线下面积(AUC)评价预测的准确性。拟合优度检验的准确率达到97.07%，十次交叉验证的准确率为95.10%。大多数铁矿床位于高潜力和中潜力地区，这些地区只占研究区的一小部分。
In this study, a novel method that integrates C4.5 decision tree, weights-of-evidence and m-branch smoothing techniques was proposed for mineral prospectivity mapping. First, a weights-of-evidence model was used to rank the importance of each evidential map and determine the optimal buffer distance. Second, a classification technique that uses a C4.5 decision tree in data mining was used to construct a decision tree classifier for the grid dataset. Finally, an m-branch smoothing technique was used as a predictor, which transformed the decision tree into a probability evaluation tree. The method makes no conditional independence assumption and can be applied for class imbalanced datasets like those collected during mineral exploration for prospectivity mapping of an area. The traits of comprehensibility, accuracy and efficiency were derived from the C4.5 decision tree. In addition, a case study for iron prospectivity mapping was performed in the eastern Kunlun Mountains, China. Sixty-two Skarn iron deposits and eight evidential maps related to iron mineralization were studied. On the final map, areas of low, moderate and high potential for iron deposit occurrence covered areas of 71,491, 14,298, and 9,532 km2, respectively. For the goodness-of-fit test, 91.94 % of the total 62 iron deposits were within a high-potential area, 8.06 % were within a moderate-potential area and 0 % were within a low-potential area. For ten-fold cross-validation, 82.26 % were within a high-potential area, 14.52 % were within a moderate-potential area and 3.22 % were within a low-potential area. To evaluate the predictive accuracy, Receiver Operating Characteristic (ROC) curves and Area Under ROC Curve (AUC) were employed. The accuracy of the goodness-of-fit test reached 97.07 %, and the accuracy of the ten-fold cross-validation was 95.10 %. The majority of the iron deposits were within high-potential and moderate-potential areas, which covered a small proportion of the study area.