Geological characterization applying k-means clustering to 3D magnetic, gravity gradient, and DC resistivity inversions: a case study at an iron oxide copper gold (IOCG) deposit

摘要

The interpretation of geophysical inversions in areas with little geologic information is not straightforward. To reduce the impact of subjective interpretation, we present the results of applying automatic clustering to identify mineralization in an iron oxide copper gold (IOCG) deposit. We apply k-means clustering to the 3D models built from unconstrained independent inversions of magnetic, gravity gradient, and DC data. K-means clustering, like most clustering techniques, requires the number of clusters as an input parameter. For this reason, we need to find the number of clusters that minimizes the objective function while keeping meaningful groups. We propose the use of the L-curve criterion from inverse theory to the plot of the k-means objective function versus number of clusters to select the optimum number of clusters. The clustering result shows a good spatial correspondence with the known geology, and the method was able to identify the mineralization. The proposed method is entirely data-driven and has proven to work in a complex geological setting such as Cristalino copper deposit.