Machine Learning Algorithms for Mineral Prospectivity Mapping (Gamze Erdogan Erten and Prof. Dr. Mahmut Yavuz)

Abstract:  

An application of a transparent and reproducible approach for identifying locations that could be of high potential for further exploration is generally a main goal for studies on mineral prospectivity. There are two main approaches for creating mineral prospectivity maps, each with several methods: (1) data-driven models, and (2) knowledge driven models. Recently, supervised and unsupervised machine learning algorithms categorized as data-driven methods, which provide an objective approach to modelling, have become popular techniques in mineral prospectivity mapping. The aim of my presentation is to share preliminary ideas about mineral prospectivity mapping using machine learning algorithms and explore the potential implementation of compositional data analysis to the mapping of mineral prospectivity..

About the speaker: 

Gamze Erdogan Erten received the BSc and MSc degrees in Mining Engineering from the University of Hacettepe and Eskisehir Osmangazi University, Turkey in 2013 and 2017, respectively. She is currently working as a research assistant and pursuing her Ph.D. (first year) at the Mining Engineering Department in Eskisehir Osmangazi University. She is working under the supervision of Mahmut Yavuz. Her research area of interest includes machine learning and mineral prospectivity mapping. She is also interested in compositional data analysis and its potential implementation to the prospectively mapping.