[an error occurred while processing this directive] 世界地质 2020, 39(4) 929-936 DOI:   10.3969/j.issn.1004-5589.2020.04.019  ISSN: 1004-5589 CN: 22-1111/P

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栈式自动编码器
极限学习机
遥感反演
地球化学异常
本文作者相关文章
王思琪
王明常
王凤艳
杨国东
张晓龙
PubMed
Article by Wang S
Article by Wang M
Article by Wang F
Article by Yang G
Article by Zhang X
基于SAE-ELM方法的多金属遥感地球化学反演
王思琪1,2, 王明常1,3, 王凤艳1, 杨国东1, 张晓龙2
1. 吉林大学 地球探测科学与技术学院, 长春 130026;
2. 中国地质调查局 西安矿产资源调查中心, 西安 710100;
3. 自然资源部城市国土资源监测与仿真重点实验室, 广东 深圳 518000
摘要: 在矿产勘查的过程中,根据地球化学数据圈定多金属异常至关重要。为解决传统线性反演模型复杂度高、运行速度慢和模型效果差等问题,提出基于栈式自编码器(SAE)和极限学习机(ELM)构建遥感地球化学非线性反演模型,以湖南郴州为研究区,对铜、铅、锌、钨、钼等元素的土壤地球化学含量及异常分布进行反演实验。实验表明, SAE-ELM反演结果精度较高,各元素相对误差的平均值为0.222,且异常分布与多金属异常参考图空间对应关系良好。
关键词 栈式自动编码器   极限学习机   遥感反演   地球化学异常  
Remote sensing geochemical inversion of multi metal materials based on SAE-ELM
WANG Si-qi1,2, WANG Ming-chang1,3, WANG Feng-yan1, YANG Guo-dong1, ZHANG Xiao-long2
1. College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China;
2. Xi'an Center of Mineral Resources Survey, China Geological Survey, Xi'an 710100, China;
3. Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR, Shenzhen 518000, Guangdong, China
Abstract: In the process of mineral exploration, it is vital to delineate polymetallic anomalies using geochemical data. To solve the problems of the traditional linear inversion model, which is highly complex, slow and with poor model effect, a remote sensing geochemical nonlinear inversion model is proposed based on stacked auto-encoder (SAE) and extreme learning machine (ELM). The geochemical contents and abnormal distribution of Cu, Pb, Zn, W and Mo in the soil from Chenzhou of Hunan Province were studied. Experiment results show that the inversion results of SAE-ELM are of high accuracy, and the average relative error of each element is 0.222, and there is a good spatial correspondence between anomaly distribution and polymetallic anomaly reference map.
Keywords: stacked auto-encoder   extreme learning machine   remote sensing inversion   geochemical anomaly  
收稿日期 2020-05-06 修回日期 2020-06-18 网络版发布日期  
DOI: 10.3969/j.issn.1004-5589.2020.04.019
基金项目:

国家自然科学基金项目(41430322)、自然资源部城市国土资源监测与仿真重点实验室开放基金资助课题(KF-2018-03-020、KF-2019-04-080)、吉林省教育厅“十三五”科学研究规划项目(JJKH20200999KJ)和上海市地质调查研究院(国土资源部地面沉降检测与防治重点实验室)开放基金项目(KLLSMP201901)资助。

通讯作者: 王明常(1975-),男,教授,博士生导师,主要从事遥感与地理信息系统方面的教学研究。E-mail:wangmc@jlu.edu.cn
作者简介:
作者Email: wangmc@jlu.edu.cn

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