Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data
Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data
复制标题
应用一类支持向量机快速识别化探数据多元异常
DOI：
10.1144/geochem2016-024
复制
发表时间：
2017-08-01
影响因子：
2.4
通讯作者：
Wu, Wei
中科院分区：
文献类型：
--
作者：
Chen, Yongliang;Wu, Wei
在复杂的地质背景下从化探数据中识别多元异常非常具有挑战性，因为复杂的地质背景可能导致化探数据的高维分布未知。单类支持向量机（OCSVM）可以在高维或不对数据分布作任何假设的情况下进行离群点检测。因此，我们应用OCSVM模型识别多元地球化学异常的水系沉积物测量数据的拉陵枣霍地区，一个复杂的地质背景，在中国青海省。从受试者工作特征（ROC）曲线、曲线下面积（AUC）和数据处理效率等方面比较了OCSVM模型与连续限制Boltzmann机（CRBM）模型的性能。结果表明，这两个模型在ROC和AUC方面表现相似，而它们的数据建模过程分别花费了6.06和279.36 s。 OCSVM模型识别的异常占研究区面积的19%，包含已知矿床的82%; CRBM模型识别的异常占研究区面积的35%，包含已知矿床的88%。
Identifying multivariate anomalies from geochemical exploration data in a complex geological setting is very challenging because the complex geological setting may lead to an unknown high-dimensional distribution of the geochemical exploration data. One-class support vector machine (OCSVM) can give useful results in outlier detection in high-dimension or without any assumptions on the distribution of data. Thus, we applied the OCSVM model to identify multivariate geochemical anomalies from stream sediment survey data of the Lalingzaohuo district, an area with complex geological setting, in Qinghai Province, China. The performance of the OCSVM model was compared with that of continuous restricted Boltzmann machine (CRBM) in terms of receiver operating characteristic (ROC) curve, area under curve (AUC) and data-processing efficiency. The results show that the two models perform similarly well in terms of ROC and AUC; while their data-modeling processes spent 6.06 and 279.36 s, respectively. The anomalies identified by the OCSVM model occupy 19% of the study area and contain 82% of the known mineral deposits; and the anomalies identified by the CRBM model occupy 35% of the study area and contain 88% of the known mineral deposits.