Comparison of support vector machines (SVMs) and the learning vector quantization (LVQ) techniques for geological domaining: a case study from Darehzar porphyry copper deposit, SE Iran

Geological domaining is an essential aspect of mineral resource evaluation. Various explicit and implicit modeling approaches have been developed for this purpose, but most of them are computationally expensive and complex, particularly when dealing with intricate mineralization systems and large datasets. Additionally, most of them require a time-consuming process for hyperparameter tuning. In this research, the application of the Learning Vector Quantization (LVQ) classification algorithm has been proposed to address these challenges. The LVQ algorithm exhibits lower complexity and computational costs compared to other machine learning algorithms. Various versions of LVQ, including LVQ1, LVQ2, and LVQ3, have been implemented for geological domaining in the Darehzar porphyry copper deposit in southeastern Iran. Their performance in geological domaining has been thoroughly investigated and compared with the Support Vector Machine (SVM), a widely accepted classification method in implicit domaining. The overall classification accuracy of LVQ1, LVQ2, LVQ3, and SVM is 90%, 90%, 91%, and 98%, respectively. Furthermore, the calculation time of these algorithms has been compared. Although the overall accuracy of the SVM method is ∼ 7% higher, its calculation time is ∼ 1000 times longer than LVQ methods. Therefore, LVQ emerges as a suitable alternative for geological domaining, especially when dealing with large datasets.