Comparative Analysis of Geochemical Data Processing Methods for Allocation of Anomalies and Background

In this paper, the capabilities of demotic unsupervised learning approaches were investigated to improve the procedure of geochemical anomaly identification. The separation of anomalous concentrations from the background is a crucial task in the mathematical analysis of geochemical data. The conventional methods rely on the statistical thresholds routinely; however, determining such boundary values fundamentally entails a normally distributed data and the involvement of expert knowledge. The unsupervised machine learning provides state-of-the-art facilities based on the information theory that leveraged to classify the geochemical data into the anomaly and background concentrations with specific characteristics. To examine the integrity of performance of geochemical data processing tools, the prevalent unsupervised learning methods of k -means, k -medoids, k -medians, expectation-maximization (EM) clustering, density-based spatial clustering of applications with noise (DBSCAN), and self-organizing maps (SOM), as well as traditional threshold-based techniques of the mean plus two standard deviations ( ) and the concentration-number (C-N) fractal model were subjected to the separation of Cu anomalies from the background within 300 rock samples collected from Shadan porphyry copper deposit, northeast Iran. The efficiency of methods was quantitatively measured using criteria comprising student’s t -test, signal to noise ratio, and the pooled coefficient of variation. The appraisal criteria have confirmed that most of the unsupervised techniques manage to isolate the geochemical anomalies from the background with a more significant contrast compared to the conventional methods of and C-N fractal model. The EM clustering has revealed the best performance among them so that it allocates the anomalies with the maximum resolution and distinguishes the weak anomalies from the high background. The anomaly Cu map obtained by the EM method has represented a significant spatial pattern that is properly consistent with the geological and mineralization evidences within the study area. The utilization of unsupervised learning methods substantially enjoys some advantages such as anomaly intensification, automaticity, being fast and non-parametric, and the capability to expand to the multivariate analysis.