Geodata Science-Based Mineral Prospectivity Mapping: A Review
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Review Paper
Geodata Science-Based Mineral Prospectivity Mapping: A Review Renguang Zuo1,2 Received 17 April 2020; accepted 5 May 2020
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. KEY WORDS: Mineral prospectivity mapping, Geodata science, Geological prospecting big data, Geoinformation, Geo-knowledge, GIS.
INTRODUCTION Mineral prospectivity mapping (MPM) aims to discover new mineral deposits by delineating favorable targets for mineral exploration. MPM can be carried out in one of two ways: by utilizing geological interpretations/observations or geoscientists experiences, or by analyzing the spatial associations between geospatial patterns and known mineral deposits. The former and latter are known as knowledge-driven and data-driven MPM, respectively (Agterberg 1989a, b; Bonham-Carter 1994; Carranza 2008). In addition, mixed models of knowledge-driven and data-driven models are applied to delineate the favorable targets for a given 1
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430074, China. 2 To whom correspondence should be addressed; e-mail: [email protected]
mineral deposit type (e.g., Porwal et al. 2006). Now, we have entered the era of big data, and MPM involves diverse and multi-disciplinary data and relies heavily on geoscience data. Thus, it is critical to understand how geoscience data may be mined and integrated to derive information, generate knowledge, and gain insights into geological processes. Data science (DS) is a novel research paradigm to mine information and knowledge from data. DS introduced by Naur (1974) has become popular in various fields. Geodata science (GDS), as a frontier interdisciplinary science which combines geosciences and DS, is the science of processing geoscience data to understand the nature and processes of Earth and planet with a focus on forecasting and evaluation of mineral resources, environment pollution, and geological hazards (Zuo and Xiong 2020). The main aims of GDS are to process and mine geoscience datasets with a focus on extracting
2020 International Association for Math
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