Rock Classification with Machine Learning: a Case Study from the Zinkgruvan Zn-Pb-Ag Deposit, Bergslagen, Sweden

Abstract

In this paper we assess two traditional machine learning (ML) methods which can be used for automatic rock type classification: (1) the Self-Organising Map (SOM) with k-means clustering, and (2) Classification and Regression Trees (CART). The dataset used for this paper were chemical compositional data of rocks acquired through X-Ray Fluorescence (XRF) analysis. The ground truth of the dataset was generated by human experts in the field of geology. The complexity of the chosen dataset influenced the evaluation performance of the two ML models. We achieve an overall accuracy of 68.02 % and 62.79 % respectively when using SOM with k-means and CART.