Knowledge graph-infused quantitative mineral resource forecasting

doi:10.13745/j.esf.sf.2024.5.3

Abstract

Abstract:

Big data and artificial intelligence have greatly transformed mineral exploration practices with the development of innovative mineral forecasting models and improvement of forecasting efficiency for strategic minerals. In the field of quantitative mineral forecasting, comprehensive intelligent forecasting by combining knowledge and data has gradually become a common consensus, however, the challenge lies in how to combine knowledge and data. Knowledge graphs integrate multi-source, heterogeneous geoscience big data and drive knowledge discovery through rules and reasoning. Here, we discuss the feasibility and technical roadmap of knowledge graph-infused intelligent and automated mineral resource forecasting, particularly in consideration of the characteristics of knowledge graphs in the era of big data and artificial intelligence. We focus mainly on the construction of multi-temporal, all-element knowledge graphs for mineral deposit-mineral exploration systems and the methodology for establishing forecasting models from the perspectives of ore commonality and distinctiveness based on knowledge graphs. The opportunities and challenges of knowledge graph embedding for geological anomaly information extraction and quantitative resource forecasting are also discussed, in the hope that the infusion of knowledge representation and reasoning from knowledge graphs into the technical workflow of quantitative mineral resource forecasting can aid geologists in building ore forecasting models and enhancing automated and intelligent mineral forecasting.

Key words: knowledge graph, mineral resource quantitative forecasting, intelligent mineral forecasting, geological big data

CLC Number:

WANG Chengbin, WANG Mingguo, WANG Bo, CHEN Jianguo, MA Xiaogang, JIANG Shu. Knowledge graph-infused quantitative mineral resource forecasting[J]. Earth Science Frontiers, 2024, 31(4): 26-36.

Figures/Tables 6

Fig.1 Development of knowledge graph technology. Modified after [8].

Fig.2 Graph representation of locality of mineral deposits

Fig.3 Ontology construction of a mineralization-exploration system using a case-driven method.Adapted from[70].

Fig.4 Constructing a mineral forecasting model based on a knowledge graph

Fig.5 Plot of group number vs. silhouette coefficient

Fig.6 Extraction and integration of deep-level anomaly information contained in a knowledge graph

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