Abstract
Identifying geochemical patterns from backgrounds and generating associated mineralization remains challenging due to the complex structure of mineral deposits. To learn how to identify geochemical anomalies that are spatially associated with mineralization, we need in-depth knowledge of the dependence process. Quantitative association rules (QARs) are applied to discover remarkable relations and dependencies between attributes in a dataset, but it is difficult to generate relationships from geochemical data. In previous studies, no methodology to find association rules is proposed to deal with geochemical data problem, and the classical methods designed for Boolean and nominal attributes require previous discretization, which makes the whole process limited in processing complex data. In this paper, we proposed a hybrid method of graph clustering and quantitative association rules (GCQAR) as a new way of identifying significant geochemical patterns. Graph Clustering (GC) is used as partitioning paradigm because of its ability to handle large-scale datasets. The GC is based on modularity to effectively generate the groups of the graph, to avoid the over-partitioning, and to cover all the rules. In each partition, a set of geochemical quantitative association rules is produced. The results obtained in the experimental study performed on data collected in the field of Xiaoshan, Henan province, China. Our GCQAR has significant benefits in terms of recognition geochemical patterns compared to the traditional methods used in the field of geochemistry.
Highlights
In recent decades, research on processing and recognition of geochemical anomalies that can be used in mineral exploration has made important progress
1) RESULTS USING DATA OF GAOBIEGOU AREA In Gaobiegou area (Fig. 10d), the geochemical anomalies are typically detected at stratigraphy, which presents a set of metamorphic sedimentary clastic rocks, divided into two lithologic sections, and fit well into Tungsten deposit
2) RESULTS USING DATA OF GUSHENLING AREA In Gushenling area (Fig. 14d), the geochemical anomalies are typically detected at magmatic rock, where volcanic activity provides a source of deposits
Summary
Research on processing and recognition of geochemical anomalies that can be used in mineral exploration has made important progress. It is essential to look for the anomalies associated with mineral deposits [1], called significant anomalies. The anomalies are often interpreted as a basic sign of mineralization [1]. The distribution of geochemical elements is heterogeneous, and usually occurs at different temporal/spatial scales, and interconnects in various ways. Computational methods are necessary to extract knowledge from geochemical elements [2] that could help to identify hidden geochemical patterns related to mineralization [1]. Association rule is a machine learning method, The associate editor coordinating the review of this manuscript and approving it for publication was Byung-Gyu Kim
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