Abstract
This paper introduces the concept of geodata science-based mineral prospectivity mapping (GSMPM), which is based on analyzing the spatial associations between geological prospecting big data (GPBD) and locations of known mineralization. Geodata science reveals the inter-correlations between GPBD and mineralization, converts GPBD into mappable criteria, and combines multiple mappable criteria into a mineral potential map. A workflow of the GSMPM is proposed and compared with the traditional workflow of mineral prospectivity mapping. More specifically, each component in such a workflow is explained in detail to demonstrate how geodata science serves mineral prospectivity mapping by deriving geoinformation from geoscience data, generating geo-knowledge from geoinformation, and allowing spatial decision-making by integrating geoinformation and geo-knowledge on the formation of mineral deposits. This review also presents several research directions for GSMPM in the future.
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