基于ART神经网络的地震剖面反褶积新方法
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摘要
提出一种基于ＡＲＴ神经网络（及其改型）从地震剖面中识别子波的新方法．发挥神经网络在模式识别和优化计算两方面的优势，先以无教师学习算法从剖面中识别出子波，再通过ＴＨ神经网络作反褶积．实验结果表明，本文提出的方法有明显的效果，特别在地震剖面中存在一些相隔较远的反射层时有很好的效果．
A new deconvolution method based on ART neural network and its derivative form, which have advantages in pattern recognition and optimization computing, is presented. With the new method the wavelet was first picked out from a seismic profile using a learning algorithm without tuitors, then deconvolution was achieved through TH neural network. Experimental results showed that the new method has good performances, especially when there exist rather seperated reflecting layers in the profile.
A new deconvolution method based on ART neural network and its derivative form, which have advantages in pattern recognition and optimization computing, is presented. With the new method the wavelet was first picked out from a seismic profile using a learning algorithm without tuitors, then deconvolution was achieved through TH neural network. Experimental results showed that the new method has good performances, especially when there exist rather seperated reflecting layers in the profile.
引文
1胡光锐，唐超．最佳剖面准则反褶积方法的研究．上海交通大学学报，1995，29（3）：12～152唐超，胡光锐．利用神经网方法进行地震信号处理的反褶积新方法．信号处理，1994，（4）：233～2373胡光锐，朱军．基于神经网络的地震剖面反褶积新方法．上海交通大学学报，1996，30（3）：154～1594JacekMZurada．Introductiontoartificialneuralsystems．USA：WestPublishingCompany，1992．389～454
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