Interpreting Mineral Deposit Genesis Classification with Decision Maps: A Case Study Using Pyrite Trace Elements

Abstract

Machine learning improves geochemistry discriminant diagrams in classifying mineral deposit genetic types. However, the increasingly recognized ‘black box’ property of machine learning has been hampering the transparency of complex data analysis, leading to the challenge in deep geochemical interpretation. To address the issue, we revisited pyrite trace elements and propose to use ‘Decision Map’, a cutting-edge visualization technique for machine learning. This technique reveals mineral deposit classifications by visualizing the ‘decision boundaries’ of high-dimensional data, a concept crucial for model interpretation, active learning, and domain adaptation. In the context of geochemical data classification, it enables geologists to understand the relationship between geo-data and decision boundaries, assess prediction certainty, and observe the data distribution trends. This bridges the gap between the insightful properties of traditional discriminant diagrams and the high-dimensional efficiency of modern machine learning. Using pyrite trace element data, we construct a decision map for mineral deposit type classification, which maintains the accuracy of machine learning while adding valuable visualization insight. Additionally, we demonstrate two applications of decision maps. First, we show how decision maps can help resolve the genetic type dispute of a deposit whose data was not used in training the models. Second, we demonstrate how the decision maps can help understand the model, which further helps find indicator elements of pyrite. The recommended indicator elements by decision maps are consistent with geologists’ knowledge. This study confirms the decision map’s effectiveness in interpreting mineral genetic type classification problems. In geochemistry classification, it marks a shift from conventional machine learning to a visually insightful approach, thereby enhancing the geological understanding derived from the model. Furthermore, our work implies that decision maps could be applicable to diverse classification challenges in geosciences.