Artificial intelligence for mineral exploration: A review and perspectives on future directions from data science

Abstract

The massive accumulation of available multi-modal mineral exploration data for most metallogenic belts worldwide provides abundant information for the discovery of mineral resources. However, managing and analyzing these ever-growing and multidisciplinary mineral exploration data has become increasingly time-consuming and labor-intensive. Artificial intelligence (AI) has demonstrated powerful prediction and knowledge integration capabilities, enabling geologists to efficiently leverage mineral exploration data. This paper reviews publications on state-of-the-art AI applications for ten mineral exploration tasks ranging from data mining to grade and tonnage estimation. These studies are based on expert systems, fuzzy logic, and various machine learning algorithms designed to optimize and improve the workflow of mineral exploration. We recognize that most AI for mineral exploration is data-driven research for now. However, AI models that couple geological knowledge and mineral exploration data will be increasingly favored in this field in the future. This paper also discusses the challenges of AI in mineral exploration research and the implications of future developments associated with novel technologies and practical deployments. Although AI has not yet been extensively tested for practical deployment in mineral exploration, its study execution exhibits the potential to trigger a fundamental research paradigm shift.