利用遗传算法优化的小波神经网络实现地热资源预测

资源与产业 ›› 2014, Vol. 16 ›› Issue (3): 101-106.

利用遗传算法优化的小波神经网络实现地热资源预测

董华松1,2,*,黄文辉1,2

1 海相储层演化与油气富集机理教育部重点实验室,北京 100083; 2 中国地质大学能源学院,北京 100083

收稿日期:2013-12-18 修回日期:2014-03-13 出版日期:2014-06-20 发布日期:2014-06-20
通讯作者: 董华松(E-mail:ogreishydhs@cup.edu.cn)

PREDICTION OF GEOTHERMAL RESOURCES BY MEANS OF WAVELET NEURAL NETWORK OPTIMIZED BY GENETIC ALGORITHM

DONG Hua-song1,2,*,HUANG Wen-hui1,2

1 Key Laboratory of Marine Reservoir Evolution and Hydrocarbon Accumulation Mechanism,Ministry of Education,Beijing 100083,China; 2 School of Energy,China University of Geosciences,Beijing 100083,China

Received:2013-12-18 Revised:2014-03-13 Online:2014-06-20 Published:2014-06-20
Contact: DONG Huasong(E-mail:ogreishydhs@cup.edu.cn)

摘要/Abstract

摘要： 文章主要介绍了一种基于遗传算法优化的小波神经网络进行地热资源评测的方法。选择对地热资源产生具有较大影响的火山、地震、布格重力、磁异常、到断层距离和地下水SiO2组分作为评测影响因子,利用小波神经网络优异的时频转换、网络自学习特性和遗传算法的全局优化特点,将两者相结合,对网络参数进行优化,并进行网络自学习。通过数据仿真得到的预测值与实际值基本一致,证明了该方法具有良好的地热资源评测前瞻性。

关键词: 地热资源预测, 小波神经网络, 遗传算法

Abstract: This paper gives an introduction to the wavelet neural network optimized by genetic algorithm which is used to predict the geothermal resources,selects volcano,earth quake,Bouguer gravity,magnetic anomaly,distance to fault and SiO2 in underground water as factors,applies excellent time-frequency transformation of wavelet neural network, network self-study and genetic algorithm to optimize the network parameter.This prediction data coincides with the real values verifying this method to be applicable in predicting geothermal resources.

Key words: prediction of geothermal resources, wavelet neural network, genetic algorithm

中图分类号:

F205
TK52

董华松,黄文辉. 利用遗传算法优化的小波神经网络实现地热资源预测[J]. 资源与产业, 2014, 16(3): 101-106.

DONG Huasong,HUANG Wenhui. PREDICTION OF GEOTHERMAL RESOURCES BY MEANS OF WAVELET NEURAL NETWORK OPTIMIZED BY GENETIC ALGORITHM[J]. Resources & Industries, 2014, 16(3): 101-106.

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