Partial Decision Tree Forest: A Machine Learning Model for the Geosciences

As a result of the continuous growth in the amount of geological data, machine learning (ML) offers an opportunity to contribute to solving problems in geosciences. However, digital geology applications introduce new challenges for machine learning due to the unique geoscience properties encountered in each problem, requiring novel research in ML. This paper proposes a novel machine learning method, entitled Partial Decision Tree Forest (PART Forest), to overcome these challenges introduced by geoscience problems and offers potential advancements in both machine learning and geoscience disciplines. The effectiveness of the proposed PART Forest method was illustrated in mineral classification. This study aims to build an intelligent ML model that automatically classifies the minerals in terms of their crystal structures (triclinic, monoclinic, orthorhombic, tetragonal, hexagonal, and trigonal) by taking into account their chemical compositions and their physical and optical properties. In the experiments, the proposed PART Forest method demonstrated its superiority over one of the well-known ensemble learning methods, random forest, in terms of accuracy, precision, recall, f-score, and AUC (area under the curve) metrics.

Automated summary

With the continuous growth of geological data, machine learning has the potential to solve problems in geosciences. However, the unique properties encountered in geoscience problems pose challenges for machine learning, necessitating novel research. This paper proposes a novel machine learning method called PART Forest, which overcomes these challenges and offers advancements in both machine learning and geoscience disciplines. The PART Forest method was demonstrated to be effective in mineral classification, surpassing the well-known ensemble learning method, random forest, in various metrics.