Mapping mineral prospectivity using an extreme learning machine regression
Mapping mineral prospectivity using an extreme learning machine regression
复制标题
使用极限学习机回归绘制矿物前景图
DOI：
10.1016/j.oregeorev.2016.06.033
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发表时间：
2017-01-01
影响因子：
3.3
通讯作者：
Wu, Wei
中科院分区：
文献类型：
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作者：
Chen, Yongliang;Wu, Wei
在本研究中，我们使用极限学习机 (ELM) 回归进行了多金属前景图绘制的案例研究。硬件平台为四核 CPU 1.8 GHz 笔记本电脑。 Almeida 的 Python 程序用于构建 ELM 回归模型，以绘制中国青海省拉岭早火地区的多金属前景图。基于研究区的地质、成矿和统计分析，使用一个目标和八个预测图模式以及两个训练集来训练 ELM 回归和逻辑回归模型。使用两个训练集进行 ELM 回归建模分别花费 61.4 s 和 65.9 s；而使用两个训练集的逻辑回归建模花费了 1704.0 s 和 1628.0 s。四个经过训练的回归模型用于绘制多金属前景图。根据每个模型预测的多金属前景，绘制受试者工作特征（ROC）曲线并估计曲线下面积（AUC）。 ROC 曲线显示，在 ROC 性能空间上，两个基于 ELM 回归的模型在某种程度上主导了两个基于逻辑回归的模型； AUC 值表明两个基于 ELM 回归的模型的整体性能略好于两个基于逻辑回归的模型。因此，在绘制多金属前景图方面，基于 ELM 回归的模型略优于基于逻辑回归的模型。使用约登指数对多金属目标进行最佳描绘，以最大限度地提高所描绘的多金属目标与已发现的多金属矿床之间的空间关联。与两个基于 Logistic 回归的模型预测的多金属目标 (4.96%) 相比，两个基于 ELM 回归的模型预测的多金属目标占研究区域的百分比较低 (2.66-2.68%)，但包含相同比例的已发现多金属矿床 (82%)。因此，ELM 回归是一种有用的快速学习数据驱动模型，在绘制矿产前景图方面略优于广泛使用的逻辑回归模型。实例研究表明，侵入古元古代金水口群白沙河组或石炭系大干沟组、石拐子组的岩浆杂岩体，受西北/东西向深断裂控制，是多金属成矿的关键，需要在今后研究区找矿过程中予以高度重视。 (C) 2016 Elsevier B.V. 保留所有权利。
In this research, we conduct a case study of mapping polymetallic prospectivity using an extreme learning machine (ELM) regression. A Quad-Core CPU 1.8 GHz laptop computer served as hardware platform. Almeida's Python program was used to construct the ELM regression model to map polymetallic prospectivity of the Lalingzaohuo district in Qinghai Province in China. Based on geologic, metallogenic, and statistical analyses of the study area, one target and eight predictor map patterns and two training sets were then used to train the ELM regression and logistic regression models. ELM regression modeling using the two training sets spends 61.4 s and 65.9 s; whereas the logistic regression modeling using the two training sets spends 1704.0 s and 1628.0 s. The four trained regression models were used to map polymetallic prospectivity. Based on the polymetallic prospectivity predicted by each model, the receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was estimated. The ROC curves show that the two ELM-regression-based models somewhat dominate the two logistic-regression-based models over the ROC performance space; and the AUC values indicate that the overall performances of the two ELM-regression-based models are somewhat better than those of the two logistic-regression-based models. Hence, the ELM-regression-based models slightly outperform the logistic-regression-based models in mapping polymetallic prospectivity. Polymetallic targets were optimally delineated by using the Youden index to maximize spatial association between the delineated polymetallic targets and the discovered polymetallic deposits. The polymetallic targets predicted by the two ELM-regression-based models occupy lower percentage of the study area (2.66-2.68%) compared to those predicted by the two logistic-regression-based models (4.96%) but contain the same percentage of the discovered polymetallic deposits (82%). Therefore, the ELM regression is a useful fast-learning data-driven model that slightly outperforms the widely used logistic regression model in mapping mineral prospectivity. The case study reveals that the magmatic complexes, which intruded into the Baishahe Formation of the Paleoproterozoic Jinshuikou Group or the Carboniferous Dagangou and Shiguaizi Formations, and which were controlled by northwest-western/east-western trending deep faults, are critical for polymetallic mineralization and need to be paid much attention to in future mineral exploration in the study area. (C) 2016 Elsevier B.V. All rights reserved.