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。
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.
作者
王思琪
王明常
王凤艳
杨国东
张晓龙
WANG Si-qi;WANG Ming-chang;WANG Feng-yan;YANG Guo-dong;ZHANG Xiao-long(College of Geo-exploration Science and Technology,Jilin University,Changchun 130026,China;Xi an Center of Mineral Resources Survey,China Geological Survey,Xi an 710100,China;Key Laboratory of Urban Land Resources Monitoring and Simulation,MNR,Shenzhen 518000,Guangdong,China)
出处
《世界地质》
CAS
2020年第4期929-936,共8页
World Geology
基金
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)
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。
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
stacked auto-encoder
extreme learning machine
remote sensing inversion
geochemical anomaly
