摘要
随着我国对石油需求的逐年增加,对石油勘探技术的要求也越来越高,而测井信号的准确识别对石油的勘探技术有着重大的意义。本文通过对测井信号的预处理,利用径向基神经网络的一种延伸--概率神经网络,对测井信号进行网络训练,达到识别的效果。研究和实验结果表明,本文方法对测井信号的识别有很高的准确性,实用性,运行速度快。
As the oil demand increased year by year, oil exploration technology requirements are also getting higher and higher, so identify the exact signal for oil exploration technology, has a great significance. Based on the logging signal preconditioning, Used a kind of RBF network extension-PNN to train the logging signal to attach the clustering effect. Research and experimental results show that this method of identification on logging signals has high accuracy, practicality, run faster.
出处
《微计算机信息》
2009年第7期262-263,289,共3页
Control & Automation
基金
基金申请人:吴国平
基金资助项目名称:油气圈闭场源参数全域灰色自适应解法
基金颁发部门:国家自然科学基金委(40674069)
