2D Density Inversion with the RBF Neural Network Method
详细信息
- 责任者：Geng Meixia (College of Geo-Exploration Science and Technology ; Jilin University ; Changchun 130026 ; China) ; Yang Qingjie
- 刊名：Oil Geophysical Prospecting
- 出版年：2013
- 卷期：48(4)
- 关键词：神经网络系统 ; neural network system ; 重力勘探 ; gravity exploration
- 页码：p.651-657
- 图表：6 illus., 26 refs.
- 分类号：1600
- issnNo：ISSN1000-7210
- isbnNo：CN13-1095/TE
摘要
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.
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