Detecting homogenous clusters using whole-rock chemical compositions and REE patterns: A graph-based geochemical approach

• Analyzing geochemical datasets without any prior knowledge about the data
• Recognizing meaningful rock groups, geological units, or geochemical zones within a set of samples
• Providing maps to represent continuous geochemical zones
• Introducing a new evaluation measure to assess algorithm performance
• Determining corresponding groups which are compositionally and genetically similar to each other
• Integrating more similar rock groups with regards to the different levels of clustering

The rock chemical composition may be affected by a wide variety of primary and secondary geological processes. Analyzing geochemical datasets of highly altered rocks is usually faced with the challenges in detecting the relationships among objects. Using traditional clustering methods in such datasets with high-dimensional data and various types of attributes commonly leads to poor quality results. Hence, a graph-based geochemical approach was proposed in this study to solve this problem. In order to determine the relationship between objects, various similarity measures related to whole-rock composition, REE pattern, and the geographical location were employed in combination to weight the edges of a similarity graph. A spectral method was effectively used to identify clusters (communities) representing rock groups, geological units, or geochemical zones in the weighted similarity graph. It could also recognize the corresponding groups being compositionally and genetically similar to each other and distinguish sub-groups or anomalous samples in the dataset with regard to the different levels of clustering. Firstly, the performance and effectiveness of the proposed approach was evaluated by testing on a GEOROC1 dataset based on some graph clustering quality functions. Then the approach was applied to the geochemical dataset of Choghart orebody comprising various altered rocks. The obtained clusters were visualized by a k-nearest neighbor classification technique to represent geochemical zones as a continuous map.