Abstract The recognition of multivariate geochemical anomalies is important for mineral exploration. Big data analytics, which involves the whole data and variables, is an alternative manner to delineate multivariate geochemical anomalies in support of machine learning algorithms due to their strong ability to capture the complex intrinsic and diverse links between geochemical characteristics and mineralization. However, this method faces the issue of data redundancy and calculation complexity, and high-dimensional problems raise great challenges for anomaly detection. This is the curse of dimensionality problem, which hinders the development of a variety of techniques for anomaly detection. In this study, a hybrid model that combines unsupervised deep belief networks (DBNs) and one-class support vector machine (OCSVM) is adopted to address the high-dimensional geochemical anomalies detection problem. In this model, the relevant features first extracted through the DBN are used as the input of the OCSVM. The decision function values of the hybrid method are employed to map the geochemical patterns related to iron mineralization. The comparative results on the performance of the hybrid model and the other three anomaly detection models (deep autoencoder model, OCSVM, and hybrid model with principal component analysis and OCSVM) in terms of the area under curve(AUC) values, suggest that the hybrid method of the DBN and OCSVM can efficiently recognize the geochemical anomalies related to iron mineralization. The DBN can extract the geochemical information, reduce the redundant features, and further enhance the scalability of the OCSVM for processing high-dimensional geochemical data. The extracted geochemical anomalies, which show a close spatial relationship with the Yanshanian intrusions, can provide significant guidance for the next round of mineral exploration.

中文翻译：

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结合深度学习和一类支持向量机识别矿产勘探的多元地球化学异常1

摘要 多元地球化学异常的识别对于矿产勘探具有重要意义。大数据分析涉及整个数据和变量，是描绘多元地球化学异常以支持机器学习算法的另一种方式，因为它们具有很强的捕捉地球化学特征和成矿之间复杂的内在和多样联系的能力。然而，这种方法面临数据冗余和计算复杂度的问题，高维问题对异常检测提出了很大的挑战。这是维度问题的诅咒，阻碍了各种异常检测技术的发展。在这项研究中，采用结合无监督深度信念网络（DBN）和一类支持向量机（OCSVM）的混合模型来解决高维地球化学异常检测问题。在该模型中，首先通过 DBN 提取的相关特征作为 OCSVM 的输入。混合方法的决策函数值用于绘制与铁矿化相关的地球化学模式。混合模型与其他三种异常检测模型（深度自动编码器模型、OCSVM 和主成分分析混合模型和 OCSVM）在曲线下面积 (AUC) 值方面的性能比较结果表明，混合模型DBN 和 OCSVM 方法可以有效识别与铁矿化相关的地球化学异常。DBN可以提取地球化学信息，减少冗余特征，进一步增强OCSVM处理高维地球化学数据的可扩展性。提取的地球化学异常与燕山期侵入体空间关系密切，可为下一轮矿产勘查提供重要指导。