Machine Learning in Geology: Challenges and Prospects

Abstract

Machine Learning methods, along with high-performance computing, is creating chances for data intensive science in various domains such as Geology. Known for its precision and efficiency, Machine learning has renovated many fields of science thoroughly. However, when it comes to Geology, Machine Learning methods aren’t fully applied, lacking an overall frame to arrange those works with scattered aims and methods. This paper presents a comprehensive review of research focusing on application of Machine Learning methods in Geology studies. The works analyzed were categorized in three aspects of Geology studies: mineral prospecting and mapping, land-cover monitoring; geochemical anomalies detection; earthquake monitoring and geological sample identification. The selection and classification of the presented articles demonstrate how Geology benefits from Machine Learning methods.