Constructing knowledge graph for the porphyry copper deposit in the Qingzhou-Hangzhou Bay area: Insight into knowledge graph based mineral resource prediction and evaluation

doi:10.13745/j.esf.sf.2021.1.2

Abstract

Abstract:

Knowledge graphs, fundamental to artificial intelligence, describe the real world in graphic forms using a language that can be understood by both humans and machines. This paper presents a case study on the construction of knowledge graph for porphyry copper deposit. The raw text data were collected and integrated from six selected porphyry and porphyry-skarn copper deposits in the Qinzhou-Hangzhou Bay metallogenic belt, one of the key metallogenic belts of China. The entities, relations and attributes in the text are labeled and extracted in reference to the conceptual model of porphyry copper deposit. The resulted knowledge graph has the basic application functions. As part of a planned integrated knowledge graph—from a single deposit, through upper-geared metallogenic series, to top metallogenic province (belt)—the present study may be extended toward understanding and improving future way of mineral resource prediction and evaluation. The interrelationship among the earth system, the metallogenic system, the exploration system, and the prediction and evaluation system (ES-MS-ES-PS) should be fully understood, and a knowledge graph for the ES-MS-ES-PS system is essential. The key scientific and technological challenges to attain such a large-scale knowledge graph for the ES-MS-ES-PS system thus include system of progressive association of domain ontology and knowledge graph, automation technology for constructing large-scale domain ontology and knowledge graph, self-evolution and complementary techniques for embedding multi-modal correlation data into knowledge graph, and ES-resource prediction theory and methods based on knowledge graph, big-data mining and artificial intelligence.

Key words: knowledge graph, knowledge acquisition, geological big data, prediction and evaluation of mineral resource, geological domain ontology, porphyry copper deposit, Qinzhou-Hangzhou Bay metallogenic belt (South China)

CLC Number:

ZHOU Yongzhang, ZHANG Qianlong, HUANG Yongjian, YANG Wei, XIAO Fan, JI Junjie, HAN Feng, TANG Lei, OUYANG Chong, SHEN Wenjie. Constructing knowledge graph for the porphyry copper deposit in the Qingzhou-Hangzhou Bay area: Insight into knowledge graph based mineral resource prediction and evaluation[J]. Earth Science Frontiers, 2021, 28(3): 67-75.

Figures/Tables 6

Fig.1 Schematic diagram showing the entities, attributes and relations in a knowledge graph

Table 1 Standardized data (partial) in csv format

节点	节点类型	关系	实体	实体类型
圆珠顶斑岩铜矿	斑岩铜矿	岩浆作用	二长花岗岩	岩浆作用类型
德兴斑岩铜矿	斑岩铜矿	变质作用	千枚岩	变质作用类型
南和斑岩铜矿	斑岩铜矿	构造作用	小王垌断裂带	构造作用类型
天堂铜多金属矿	斑岩铜矿	地层演化	天子岭组	地层类型
石菉铜矿	斑岩铜矿	围岩蚀变	硅化	围岩蚀变作用类型
岭后铜矿	斑岩铜矿	元素富集与贫化	Cu含量	元素富集与贫化类型

Fig.2 Knowledge graph for the porphyry copper deposit in the Qinzhou-Hangzhou Bay metallogenic belt, South China

Fig.3 Nodes having directional relations to the Dexing porphyry copper deposit(Different colors indicate different types of geological properties)

Fig.4 A sectional visualization of a knowledge graph for epithermal metallogenic system

Fig.5 A sectional visualization of the knowledge graph for the Qinzhou-Hangzhou Bay metallogenic belt

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