| 重力异常的BP神经网络三维物性反演 |
| |
| 引用本文： | 郭文斌,朱自强,鲁光银.重力异常的BP神经网络三维物性反演[J].地球物理学进展,2012,27(2):409-416. |
| |
| 作者姓名： | 郭文斌 朱自强 鲁光银 |
| |
| 作者单位： | 中南大学信息物理工程学院,长沙,410083 |
| |
| 基金项目： | 国家高科技发展计划项目(863计划) |
| |
| 摘 要： | 三维物性反演参数多,计算量巨大,传统的方法难以实现.本文使用BP神经网络实现重力三维物性反演,介绍了BP神经网络的基本原理及特性,并构造一个适用于重力位场反演的BP神经网络.并用其对模型进行反演计算,结果表明:BP网络具有较好的泛化能力和容错能力,反演速度快、准确,并且较好的反应了场源的分布情况.
|
| 关 键 词： | 重力 三维 物性反演 BP神经网络 |
3-D gravity inversion for physical properties using BP network |
| |
| GUO Wen-bin
,
ZHU Zi-qiang
,
LU Guang-yin.3-D gravity inversion for physical properties using BP network[J].Progress in Geophysics,2012,27(2):409-416. |
| |
| Authors: | GUO Wen-bin ZHU Zi-qiang LU Guang-yin |
| |
| Institution: | *(School of Info-physics and Geomatics Engineering,Central South University,Changsha 410083,China) |
| |
| Abstract: | The traditional methods are hardly used in 3-D inversion for physical properties,because of the large number of parameters and the quantities.The BP artificial neural network is widely used in geophysical inversion,but it’s mainly used in the inversions which have few inversion parameters.In this paper,we used BP artificial neural network to develop a method of 3-D gravity inversion for physical properties which has the large number of parameters and the quantities.In this paper,we introduced the principle of BP artificial neural network,it’s characteristic and the characteristic of different learning algorithms,built kinds of BP artificial neural network and analysed that how the structure of BP artificial neural network,the scalar of the samples and the learning algorithm effect the inversion reaults when there is lots of parameters.Also,we inroduced that how to built a suitable BP artificial neural network using structure method and abridgment.Then,we built a suitable BP artificial neural network for 3-D gravity physical inversion with the matlab neural network toolbox which is aslo introduced in the paper.Compared the inversion results of different date produced by different models,We’ll analysis the generalization ability of BPnetwork.Compared with inversion result of the noise date,we can analysis the fault-tolerant ability of BPnetwork.The result proved that the inversion using a suitable BP artificial neural network is fast and exact.Even there is a large number of parameters,we can well obtained the distribution of source.It’s have a good generalization ability,and it’s fault-tolerant ability is still good. |
| |
| Keywords: | gravity Three-dimensional inversion for physical properties BP network |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
|
