A Heterogeneous Graph Construction Method for Mineral Prospectivity Mapping

IF 4.8 2区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
Luyi Shi, Ying Xu, Renguang Zuo
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引用次数: 0

Abstract

Graph-based models have been utilized for mineral prospectivity mapping (MPM), and they have demonstrated excellent performance owing to their adaptable graph structure, which is conducive to comprehensively considering the spatial anisotropy of mineralization compared with pixel- or image-based models. However, widely used graph-based models cannot fully consider the relationship between geological entities and mineralization. A heterogeneous graph is a type of graph structure containing rich heterogeneous information, allowing the consideration of various relationships and the assignment of suitable attributes to various types of nodes. Nodes in heterogeneous graphs can fully integrate heterogeneous information based on specific relations (i.e., edges). This study introduced a novel method for constructing heterogeneous graphs for MPM. The nodes in the graph consist of different types of geological entities, and the edges (relations) represent the links between the geological entities. The constructed heterogeneous graph cannot only effectively express the spatial anisotropy of mineralization but also consider the shape of geological entities and the relationships among geological entities, which is beneficial for modeling complex ore-forming geological processes. This heterogeneous graph was then trained using graph neural networks to obtain a mineral prospectivity map for southwestern Fujian Province, China. In addition, the proposed graph construction method demonstrated higher feasibility and accuracy in MPM compared to conventional graph construction method and convolutional neural networks.

Abstract Image

用于绘制矿产远景图的异质图构建方法
基于图形的模型已被用于矿产远景测绘(MPM),与基于像素或图像的模型相比,其适应性强的图形结构有利于全面考虑矿化的空间各向异性,因而表现出卓越的性能。然而,广泛使用的基于图的模型无法全面考虑地质实体与矿化之间的关系。异质图是一种包含丰富异质信息的图结构,可以考虑各种关系,并为各类节点分配合适的属性。异质图中的节点可以根据特定关系(即边)充分整合异质信息。本研究为 MPM 引入了一种构建异构图的新方法。图中的节点由不同类型的地质实体组成,边(关系)代表地质实体之间的联系。构建的异质图不仅能有效表达成矿的空间各向异性,还能考虑地质实体的形状和地质实体之间的关系,有利于复杂成矿地质过程的建模。然后,利用图神经网络对该异质图进行训练,得到了中国福建省西南部的矿产远景图。此外,与传统的图构建方法和卷积神经网络相比,所提出的图构建方法在 MPM 中表现出更高的可行性和准确性。
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来源期刊
Natural Resources Research
Natural Resources Research Environmental Science-General Environmental Science
CiteScore
11.90
自引率
11.10%
发文量
151
期刊介绍: This journal publishes quantitative studies of natural (mainly but not limited to mineral) resources exploration, evaluation and exploitation, including environmental and risk-related aspects. Typical articles use geoscientific data or analyses to assess, test, or compare resource-related aspects. NRR covers a wide variety of resources including minerals, coal, hydrocarbon, geothermal, water, and vegetation. Case studies are welcome.
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