Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data
Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data
复制标题
使用一类支持向量机从数字地质调查数据中识别多元地质异常来绘制矿产前景图
DOI：
10.1080/08120099.2017.1328705
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发表时间：
2017-05
影响因子：
1.2
通讯作者：
Wu Wei
中科院分区：
文献类型：
--
作者：
Chen Yongliang;Wu Wei
摘要 矿产目标是局部地质异常。在多个晶胞的研究区域中，可以通过从晶胞群体中识别异常晶胞来绘制矿物远景图。一类支持向量机 (OCSVM) 模型可以在高维数据的异常检测中产生有用的结果，或者无需对内在数据的分布进行任何假设。应用OCSVM模型对吉林省老土顶子-小四坪地区金矿远景图进行了地质背景复杂的测绘。基于训练好的OCSVM模型计算属于异常的每个晶胞的决策函数值，并用于表达该晶胞的黄金前景。使用受试者工作特征 (ROC) 曲线、曲线下面积 (AUC) 和数据处理效率来比较 OCSVM 模型和受限玻尔兹曼机 (RBM) 模型在绘制黄金前景方面的性能。结果表明，OCSVM 模型在 ROC、AUC 和数据处理效率方面优于 RBM 模型。通过使用约登指数来优化描绘金矿目标，以最大化所描绘的金矿目标与已知金矿床之间的空间关联。 OCSVM模型描绘的金矿目标占研究区域的11%，包含已知金矿床的88%； RBM模型圈定的金矿目标区占研究区域的10%，包含已知金矿床的81%。因此，OCSVM模型是一种可行的矿产远景测绘方法。
ABSTRACT Mineral targets are local geological anomalies. In a study area of a number of unit cells, mapping mineral prospectivity can be implemented by identifying anomaly cells from the unit cell population. One-class support vector machine (OCSVM) models can yield useful results in anomaly detection in high-dimensional data or without any assumptions on the distribution of the inlying data. The OCSVM model was applied to mapping gold prospectivity of the Laotudingzi-Xiaosiping district, an area with a complex geological background, in Jilin Province, China. The decision function value of each unit cell belonging to an anomaly was computed on the basis of the trained OCSVM model and used to express gold prospectivity of the cell. The receiver operating characteristic (ROC) curve, area under curve (AUC) and data-processing efficiency were used to compare the performance of the OCSVM model and a restricted Boltzmann machine (RBM) model in mapping gold prospectivity. The results show that the OCSVM model outperforms the RBM model in terms of ROC, AUC and data-processing efficiency. Gold targets were optimally delineated by using the Youden index to maximise the spatial association between the delineated gold targets and known gold deposits. The gold targets delineated by the OCSVM model occupy 11% of the study area and contain 88% of the known gold deposits; and the gold targets delineated by the RBM model occupy 10% of the study area and contain 81% of the known gold deposits. Therefore, the OCSVM model is a feasible mineral prospectivity mapping approach.
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影响因子：
5.4
作者：
Geranian, Hamid;Tabatabaei, Seyed Hassan;Carranza, Emmanuel John M.
通讯作者：
Carranza, Emmanuel John M.
影响因子：
4.4
作者：
Abedi, Maysam;Norouzi, Gholam-Hossain;Bahroudi, Abbas
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影响因子：
3.3
作者：
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通讯作者：
Yongliang Chen
影响因子：
3.3
作者：
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通讯作者：
T. McCuaig;S. Beresford;J. Hronsky
影响因子：
5.4
作者：
D. Harris;Guocheng Pan
通讯作者：
D. Harris;Guocheng Pan