☆ 4.7 Article

Self-paced ensemble for constructing an efficient robust high-performance classification model for detecting mineralization anomalies from geochemical exploration data

ORE GEOLOGY REVIEWS (2023)

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Summary: Both anomaly detection algorithms and supervised classification algorithms can be used to detect mineralization-related geochemical anomalies in areas with discovered mineral deposits. However, neither of these models perform well due to their limitations in utilizing known mineral deposits as supervisors or handling extreme class-imbalance in geochemical exploration data. Therefore, a self-training model based on support vector classifiers was adopted and outperformed other models in detecting gold mineralization-related geochemical anomalies.

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Summary: This study proposed a method using total distance from a sample to all remaining samples in a population to define distance anomaly factors for outlier detection in geochemical data. The distance anomaly factors outperformed continuous restricted Boltzmann machine and one-class support vector machine in detecting multivariate geochemical anomalies associated with gold deposits. It is a potentially useful technique for geochemical anomaly detection.

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