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摘要:
通过分析地球化学数据的元素值属性和空间位置，提出一种基于邻域约束聚类的方法，使用该方法对地球化学元素聚类后，能提取矩形、环状、半环状等特殊形状，进而提取地球化学异常。选取河南崤山地区2个实验区的地球化学数据进行实验，实验一的结果表明，出现矩形的位置与已知钨矿矿点位置一致；实验二的结果表明，出现环形的位置与已知铜矿矿点位置一致。实验证明了基于邻域约束聚类的方法在提取地球化学异常方面的有效性。
Abstract: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.
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Key words:
- geochemical anomaly /
- neighborhood constraint /
- clustering
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表 1 三种聚类算法的优缺点分析
Table 1. Analysis of advantages and disadvantages of three clustering algorithms
聚类算法 优点 缺点 K-Means 简单易实现，适用性强 需要用户事先指定类簇个数；聚类结果对初始类簇中心的选取较为敏感；容易陷入局部最优 DBSCAN 聚类速度快且能够有效处理噪声点和发现任意形状的空间聚类 当空间聚类的密度不均匀、聚类间距差相差很大时，聚类质量较差；对于高维数据，存在“维数灾难”；参数难设置，且无法指定聚类数量 谱聚类 算法在处理稀疏和高维数据上具有一定的优势 若聚类的维度非常高，由于降维的幅度不够，谱聚类的运行速度会降低 
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