Application of ant colony algorithm to geochemical anomaly detection

• Use an ant colony algorithm to detect geochemical element anomalies.
• Use ROC curve to assess the performance of metallogenic indicators.
• Use AUC metric to assess the performance of metallogenic indicators.
• Use the Youden index to determine the optimal threshold of geochemical data.

The methods for geochemical anomaly detection are commonly based on statistical models that require assumption of the sample population to satisfy a particular distribution. In practice, the assumption of a particular distribution may degrade the performance of geochemical anomaly detection. In this paper, an ant colony algorithm is used to detect geochemical anomalies. The new method does not require assumption that the geochemical data satisfy a particular distribution. Applying this method to detect geochemical anomalies, we only need to put a number of “virtual ants” randomly into a geochemical grid map and let each ant complete its iterative search process. When the algorithm gets converged, the ants tend to aggregate in the geochemical anomalous regions where geochemical element concentration values are significantly greater than surrounding background. The number of times each grid point is visited by ants can be recorded in ant density data for geochemical anomaly identification. The ant density data are almost not affected by regional variations of geochemical background, thus they are suitable for identifying geochemical anomalies using a threshold method. As an illustration, the ant colony algorithm is applied to detect geochemical anomalies in interpolated concentration data of Au, Ag, Cu, Pb, and Zn in the Altay district in northern Xinjiang in China. The results show that the ant colony algorithm can properly identify geochemical anomalies. Anomalies detected by the ant colony algorithm occupy 9.5% of the study area and contain 36% of the known mineral deposits; and anomalies identified using the Youden index method occupy 16.4% of the study area and contain 56% of the known mineral deposits. Therefore, the ant colony algorithm can serve as a feasible swarm intelligence paradigm for geochemical anomaly detection.