时序RBF神经网络在地震数据重构中的应用  

The Application of Timing RBF Neural Network in Seismic Data Reconfiguration

作　　者：陆华[1] 黄维婷[1] 刘斌[1] 

机构地区：[1]成都理工大学管理科学学院,四川成都610059

出　　处：《西安文理学院学报（自然科学版）》2011年第4期10-13,共4页Journal of Xi’an University(Natural Science Edition)

基　　金：中国地质调查局地质调查资助项目(1212010916040)

摘　　要：为了降低采集过程中各种噪声干扰以及老数据的磁带掉粉等因素造成的地震数据异常,处理与地层性质无关的抖动和毛刺干扰.采用时序RBF神经网络在线重构的方法.结果表明:在保留实际信息的前提下能有效的去除地震数据中的异常,使地震相与实际的地质体更加贴近.该方法具有很好的灵活性、及时性和低耗性,重构后的数据更加有利于后期解释工作和油气藏的描述.This study aims to reduce the noise interference and the powder dropping in the data collection process which lead to all sorts of abnormalities of seismic data and to handle the jitter and burr interference unrelated to formation properties.We have adopted the online reconfiguration of timing RBF neural network.The result shows that the method is effective in removing the abnormal seismic data without the loss of factual information and makes the seismic facies closer to geological body.The method features flexibility,timeliness and low consumption.The reconfigured data is conducive to the interpretation at the later stage and the description of oil-gas reservoir.

关 键 词：地震异常 时序RBF 重构 油气描述 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]