应用RBF神经网络反演二维重力密度分布  

2-D density inversion with the RBF neural network method

作　　者：耿美霞[1] 杨庆节[1] 

机构地区：[1]吉林大学地球探测科学与技术学院,吉林长春130021

出　　处：《石油地球物理勘探》2013年第4期651-657,676+506,共7页Oil Geophysical Prospecting

摘　　要：密度反演以物性变化勾绘场源范围,具有模拟复杂地质体的能力和较强的适应能力,是提高重力方法解决地质问题能力的重要途径。本文利用径向基函数(RBF,Radical Basis Function)神经网络突出的非线性映射能力和泛化性,实现了重力密度二维非线性反演。模型计算证明了该方法的有效性,同时探讨了网络结构、参数的选择以及随机噪声对反演结果的影响。应用此法对中国西北地区阿门子处的重力异常进行反演计算,证实了此方法的实用性。According to the change of physical properties,density inversion can delineate complex source distributions and simulate complex geological bodies.So it has become an important approach for solving geologic problems with gravity method.In this paper,2-D density non-linear inversion is developed using the RBF(Radical Basis Function) neural network method,which has the advantage of nonlinear mapping and better generalization capabilities.This method is tested on both synthetic and field data sets.The results show that the proposed method is effective and robust.In addition,the structures and parameters of the network,as well as noise effects on the inversion are discussed in this paper.

关 键 词：密度 反演 非惟一性 RBF神经网路 

分 类 号：P631[天文地球—地质矿产勘探]