基于水系沉积物地球化学采样的地形加权图卷积网络基岩填图方法  

Bedrock mapping based on terrain weighted directed graph convolutional network using stream sediment geochemical samplings

作　　者：张宝一 李曼懿 浣雨柯 Umair KHAN 王丽芳 汪凡云 Bao-yi ZHANG;Man-yi LI;Yu-ke HUAN;Umair KHAN;Li-fang WANG;Fan-yun WANG(Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Ministry of Education),School of Geosciences&Info-Physics,Central South University,Changsha 410083,China;PowerChina Zhongnan Engineering Corporation Limited,Changsha 410014,China;Department of Surveying and Mapping Geography,Hunan Vocational College of Engineering,Changsha 410151,China)

机构地区：[1]中南大学地球科学与信息物理学院,有色金属成矿预测与地质环境监测教育部重点实验室,长沙410083 [2]中国电建集团中南勘测设计研究院有限公司,长沙410014 [3]湖南工程职业技术学院测绘地理学院,长沙410151

出　　处：《Transactions of Nonferrous Metals Society of China》2023年第9期2799-2814,共16页中国有色金属学报（英文版）

基　　金：The authors are grateful for the financial supports from the National Natural Science Foundation of China(Nos.42072326,41772348);the National Key Research and Development Program,China(No.2019YFC1805905).

摘　　要：为了探索高效的第四系覆盖及露头较少区域的基岩智能填图方法,应用图卷积网络(GCN)对青海省察汗乌苏河地区水系沉积物地球化学采样的下伏基岩进行分类。基于Delaunay三角化采样点被组织为一个地形加权的有向图来表达水系沉积物地球化学采样点之间的河流上下游关系。实验结果表明:半监督的GCN模型仅使用了20%的采样点标签,分类精度达到68.20%(10类基岩)和78.31%(5类基岩)。该方法能有效利用水系沉积物地球化学采样中的元素含量进行基岩填图,且能提高基岩填图的效率并能进行大面积应用。To explore an efficient strategy for intelligent bedrock mapping that can be applied in the areas with coexisting Quaternary coverages and bedrock outcrops,a graph convolutional network(GCN)was implemented for bedrock classification using stream sediment geochemical samplings in the Chahanwusu River area,Qinghai Province,China.The sampling points were organized into a terrain weighted directed graph(TWDG)using Delaunay triangulation to capture the upstream−downstream relationships among the geochemical sampling points.The experimental results indicate that the semi-supervised GCN models,only using 20%of the labeled sampling points,achieved accuracies of 68.20%and 78.31%in ten-type and five-type bedrock discrimination,respectively.In conclusion,it is feasible to map the bedrock type through the concentrations of elements on the stream sediment geochemical sampling points.The proposed data-driven GCN bedrock classification method not only improves the efficiency of bedrock mapping but also may be applied in a large area.

关 键 词：图卷积网络 深度学习 水系沉积物地球化学采样 基岩填图 第四系覆盖物 

分 类 号：P512.2[天文地球—地质学] P632