典型文献
Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province, China as an example
文献摘要：
A method is proposed for the prospecting prediction of subsurface mineral deposits based on soil geochemistry data and a deep convolutional neural network model. This method uses three techniques (window offset, scaling, and rotation) to enhance the number of training data for the model. A window area is used to extract the spatial distribution characteristics of soil geochemistry and measure their correspondence with the occurrence of known subsurface deposits. Prospecting prediction is achieved by matching the characteristics of the window area of an unknown area with the relationships established in the known area. This method can efficiently predict mineral prospective areas where there are few ore deposits used for generating the training dataset, meaning that the deep-learning method can be effectively used for deposit prospecting prediction. Using soil active geochemical measurement data, this method was applied in the Daqiao area, Gansu Province, for which seven favorable gold prospecting target areas were predicted. The Daqiao orogenic gold deposit of latest Jurassic and Early Jurassic age in the southern domain has more than 105 t of gold resources at an average grade of 3?4 g/t. In 2020, the project team drilled and verified the K prediction area, and found 66 m gold mineralized bodies. The new method should be applicable to prospecting prediction using conventional geochemical data in other areas.
文献关键词：
中图分类号：
作者姓名：
Yong-sheng Li;Chong Peng;Xiang-jin Ran;Lin-Fu Xue;She-li Chai
作者机构：
Development and Research Center of China Geological Survey,Ministry of Natural Resources,Beijing 100037,China;Technical Guidance Center for Mineral Exploration,Ministry of Natural Resources,Beijing 100083,China;College of Geographical Sciences,Shanxi Normal University,Linfen 041004,China;College of Geo-Exploration Science and Technology,Jilin University,Changchun 130026,China;College of Earth Sciences,Jilin University,Changchun 130061,China
文献出处：
引用格式：
[1]Yong-sheng Li;Chong Peng;Xiang-jin Ran;Lin-Fu Xue;She-li Chai-.Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province, China as an example)[J].中国地质（英文）,2022(01):71-83
A类：
Daqiao,Prospecting
B类：
Soil,geochemical,prospecting,prediction,method,deep,convolutional,neural,networks,Taking,Gold,Deposit,Gansu,Province,China,example,proposed,subsurface,deposits,soil,geochemistry,model,This,uses,three,techniques,window,offset,scaling,rotation,enhance,number,training,used,extract,spatial,distribution,characteristics,their,correspondence,occurrence,achieved,by,matching,unknown,relationships,established,can,efficiently,prospective,areas,where,there,few,generating,dataset,meaning,that,learning,effectively,Using,active,measurement,this,was,applied,which,seven,favorable,gold,target,were,predicted,orogenic,latest,Jurassic,Early,southern,domain,has,more,than,resources,average,grade,In,project,team,drilled,verified,found,mineralized,bodies,new,should,applicable,using,conventional,other
AB值：
0.483335
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