基于土壤化学成分的基岩岩石类型识别  

The identification of bedrock types based on soil chemical composition

作　　者：王嘉怡 郝立波[1] 赵新运[1] 马成有[1] 陆继龙[1] 赵玉岩[1] 魏俏巧[1] WANG Jia-Yi;HAO Li-Bo;ZHAO Xin-Yun;MA Cheng-You;LU Ji-Long;ZHAO Yu-Yan;WEI Qiao-Qiao(College of Geo-Exploration Science and Technology,Jilin University,Changchun 130026,China)

机构地区：[1]吉林大学地球探测科学与技术学院,吉林长春130026

出　　处：《物探与化探》2018年第6期1180-1185,共6页Geophysical and Geochemical Exploration

基　　金：中央级公益性科研院所基本科研业务费专项(AS2016P02);中国地质调查局项目(20089941)

摘　　要：浅覆盖区基岩露头少,地质填图精度低。根据岩石风化成土过程中的化学成分继承性,笔者提出了基于土壤化学成分识别基岩岩石类型的多层感知器神经网络模型。以大兴安岭北部阿龙山地区为例,根据火山岩基岩上覆土壤样品常量元素和亲石微量元素分析数据,有效地识别出了玄武岩类、安山岩类、英安岩类和流纹岩类4类基岩类型,识别的正确率达到了90%。基于土壤化学成分识别基岩岩石类型的多层感知器神经网络模型具有方便、快捷、高效等优点,是提高浅覆盖区地质填图质量的有效途径之一。Geological mapping of areas with shallow overburden generally has less bedrock outcrop to work with,and is thus characterized by poor accuracy.Soils formed by weathering of rocks have significant inheritance of chemical composition from the bedrocks.In view of such a situation,the authors propose a soil chemical composition based multi-layer perceptron neural network model to recognize bedrock types.Taking Alongshan area in the northern part of the Da Hinggan Mountains as an example,the authors used geochemical data of major and lithophile trace elements of soil samples overlying volcanic bedrocks to identify bedrock types,and identified 4 types of bedrocks,i.e.,basalt,andesite,dacite and rhyolite.The results show that the prediction accuracy of the model in the identification of bedrock types in the shallow overburden area of the Da Hinggan Mountains reaches up to 90%.The authors have reached the conclusion that soil chemical composition based multi-layer perceptron neural network model used for the identification of bedrock types has the advantage of high convenience,high speed and efficiency,and can provide an effective way for improving the geological mapping quality in areas with shallow overburden.

关 键 词：神经网络模型 地质填图 浅覆盖区 土壤 基岩 

分 类 号：P632[天文地球—地质矿产勘探]