Intelligent application of knowledge graphs in mineral prospecting: A case study of porphyry copper deposits in the Qin-Hang metallogenic belt

doi:10.13745/j.esf.sf.2024.5.2

Abstract

Abstract:

The mineral exploration knowledge graph (MEKG) is a component of Earth system-mineralization system-mining system correlation graphs, which represents an intersection between Earth science and data science and provides a novel approach for the prediction and evaluation of mineral resources. The conventional mineral exploration methods suffer from information asymmetry/inefficiency/inaccuracy, thus have limitations in effective utilization of geological data. To address this issue, we collect knowledge data relating to porphyry copper ore in the Qin-Hang mineralization belt using both primary and literature data sources, and construct a MEKG with automated knowledge extraction and reasoning using natural language processing (NLP) techniques. Briefly, the MEKG model represents the entities and attributes of porphyry copper ore and their relationships in the Qin-Hang mineralization belt; based on this framework, NLP techniques are used to semantically correlate and reason over the knowledge graph, enabling automated knowledge extraction and reasoning. In addition, we develop a Q&A and visualization system that allows users to query entities/attributes and their relationships to obtain relevant information and visualize the data structure and data relationship. This study demonstrates the effectiveness and accuracy of knowledge-based intelligent applications in porphyry copper ore exploration in the Qin-Hang belt through experimentation and testing. Compared with traditional methods, this application provides more comprehensive and accurate mining suggestions in a short time to aid geological exploration decision-making. Also this study can serve as reference for other mineral exploration fields. In the future, we aim to further enhance the performance and functionality of this knowledge model by broadening the graph algorithm applications and recommendation systems, so as to meet the needs of mineral explorations under different scenarios and expand the model’s application potential to other related fields.

Key words: knowledge graph, ore deposit ontology, porphyry copper deposit, Qin-Hang metallogenic belt, mineral prospecting mapping

CLC Number:

ZHANG Qianlong, ZHOU Yongzhang, GUO Lanxuan, YUAN Guiqiang, YU Pengpeng, WANG Hanyu, ZHU Biaobiao, HAN Feng, LONG Shiyao. Intelligent application of knowledge graphs in mineral prospecting: A case study of porphyry copper deposits in the Qin-Hang metallogenic belt[J]. Earth Science Frontiers, 2024, 31(4): 7-15.

Figures/Tables 8

References 22

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文本	抽取结果
圆珠顶斑岩型铜矿位于钦杭结合带南段、云开地区北部。区域内出露最多的地层为寒武系水口群,一套节理裂隙发育的浅海相复理石砂页岩建造,这是圆珠顶斑岩型铜钼矿的重要赋矿层位。区内还出露有泥盆系砂页岩、石炭系浅海-潟湖相灰岩-白云质灰岩、白垩系紫红色砂页岩、古近系钙质粉砂岩以及第四系沉积物^[19]	<圆珠顶斑岩型铜矿,位于,钦杭结合带南段>
	<圆珠顶斑岩型铜矿,位于,云开地区北部>
	<圆珠顶斑岩型铜矿,出露,寒武系水口群>
	<圆珠顶斑岩型铜矿,出露,泥盆系砂页岩>
	<圆珠顶斑岩型铜矿,出露,石炭系浅海-潟湖相灰岩-白云质灰岩>
	<圆珠顶斑岩型铜矿,出露,白垩系紫红色砂页岩>
	<圆珠顶斑岩型铜矿,出露,古近系钙质粉砂岩>
	<圆珠顶斑岩型铜矿,出露,第四系沉积物>
	<寒武系水口群,是,一套节理裂隙发育的浅海相复理石砂页岩建造>
	<寒武系水口群,是,重要赋矿层位>

文本	抽取结果
圆珠顶斑岩型铜矿位于钦杭结合带南段、云开地区北部。区域内出露最多的地层为寒武系水口群,一套节理裂隙发育的浅海相复理石砂页岩建造,这是圆珠顶斑岩型铜钼矿的重要赋矿层位。区内还出露有泥盆系砂页岩、石炭系浅海-潟湖相灰岩-白云质灰岩、白垩系紫红色砂页岩、古近系钙质粉砂岩以及第四系沉积物^[19]	<圆珠顶斑岩型铜矿,位于,钦杭结合带南段>
	<圆珠顶斑岩型铜矿,位于,云开地区北部>
	<圆珠顶斑岩型铜矿,出露,寒武系水口群>
	<圆珠顶斑岩型铜矿,出露,泥盆系砂页岩>
	<圆珠顶斑岩型铜矿,出露,石炭系浅海-潟湖相灰岩-白云质灰岩>
	<圆珠顶斑岩型铜矿,出露,白垩系紫红色砂页岩>
	<圆珠顶斑岩型铜矿,出露,古近系钙质粉砂岩>
	<圆珠顶斑岩型铜矿,出露,第四系沉积物>
	<寒武系水口群,是,一套节理裂隙发育的浅海相复理石砂页岩建造>
	<寒武系水口群,是,重要赋矿层位>