塔里木盆地航磁局部异常的自学模式识别分类及评价  

Classification and evaluation of local aeromagnetic anomaly bodies in the Tarim basin by self-learning pattern recognition

作　　者：刘展[1] 许炳如[2] 

机构地区：[1]西安工程学院 [2]西安石油学院

出　　处：《石油地球物理勘探》1999年第1期71-78,共8页Oil Geophysical Prospecting

基　　金：国家"八五"重点科技攻关项目!85-101-05-06-01A

摘　　要：塔里木盆地航磁△T测量取得了丰富的信息，圈出了许多局部异常。评价、解释这些异常，为油气勘探提供靶区十分重要。本文结合塔里木盆地的地质、钻井、地震、重力等资料，对有意义的379个局部异常，采用自学模式识别方法进行分类，给出了各类异常的地质解释，并指出了该盆地的含油气远景区。The aeromagnetic survey in Tarim basin offers abundant informations and shows many local aeromagnetic anomaly bodies. It is very important for us to evaluate these anomaly bodies so as to find hydrocarbon exploration targets. With the joint use of geological, drilling, seismic and gravimetric data, 379 significant anomaly bodies have been classified and interpreted by using self-learning pattern recognition. Potential hydrocarbon areas in this basin have been mapped then.

关 键 词：自学模式识别 塔里木盆地 航空磁法勘探 分类 

分 类 号：P631.222[天文地球—地质矿产勘探]