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.

中文翻译：

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基于地理数据科学的矿产前景图谱研究

本文在分析地质勘探大数据（GPBD）与已知成矿位置之间的空间关联的基础上，介绍了基于地理数据科学的矿产前景地图（GSMPM）的概念。地理数据科学揭示了GPBD与矿化之间的相互关系，将GPBD转换为可映射的标准，并将多个可映射的标准组合成一个矿产潜力图。提出了GSMPM的工作流程，并将其与传统的矿物远景测绘工作流程进行了比较。更具体地说，将详细说明此类工作流程中的每个组件，以展示地理数据科学如何通过从地球科学数据中获取地理信息，从地理信息中生成地理知识，并通过整合地理信息和地质知识对矿床的形成进行空间决策。这篇综述还提出了未来GSMPM的几个研究方向。