Geological anomaly extraction based on neighborhood constraint clustering

Geochemical anomalies often have a strong correlation with ore deposits. The study of effective methods for extracting geochemical anomalies is of great significance for prospecting. The advent of the era of big data and artificial intelligence poses new challenges for the extraction of geochemical anomalies that are automatic and independent of expert knowledge. Geostatistical research shows that it is a new geochemical prospecting idea to identify geochemical anomalies by identifying the special spatial forms of geochemical anomalies, such as lattices, bands, and rings. By analyzing the element value attribute and spatial position of geochemical data, this paper proposes a method based on neighborhood constrained clustering. After clustering geochemical elements, it can extract special shapes such as rectangle, ring and semi-ring and extract geochemical anomalies. In this paper, the geochemical data of two experimental areas in the Xiaoshan area of Henan Province were selected for experiments. The results of Experiment 1 show that the position of the rectangle appears consistent with the location of the known tungsten ore site, whereas the results of Experiment 2 show that the position of the ring is consistent with the location of the known copper orebody. The experiment proves the effectiveness of the method based on neighborhood constrained clustering in extracting geochemical anomalies.