利用多分辨率小波网络进行地震资料反演
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摘要
在讨论小波网络理论方法的基础上,研究了利用地震纪录小波多尺度分解属性资料进行虚井声波时差反演的技术方法。分析了利用地震信号进行小波分解和网络学习、训练的理论方法。研究中发现:对于相邻的地震道,较小一段的相似性比整个地震道的相似性要好。据此,利用小波时-频分析技术方法,可以把相邻道的信息外推到其它地震道上。通过以上综合研究及对实际资料进行反演计算、分析,认为小波网络与人工神经网络相比其网络结构要容易选定,并且收敛速度快。同时,利用地震资料分段时-频分析的相似性较好和小波网络学习、训练及记忆能力较强的特点,可以较好地把井旁道的高、低频信息转换到相邻道上。这样在提高分辨率的同时,又增加了反演结果的真实可靠性。
In the light of the theoretical technique of wavelet networks, we study the method for inversing the time difference of artificial acoustic wave (Δt) by using multi-scale decompositon of wavelet from seismic records. We analyze also the theoretical method of wavelet decomposition, as well as network learning and training from seismic signals. In these studies, we find that for the adjacent seismic channels, the similarity of a smaller section is better than that of the whole channel. Therefore, the information obtained from the adjacent seismic channel can be extrapolated to the other channel by using time-frequency analysis method of wavelet. The results of the afore-mentioned studies, as well as the inversion and calculation of practical seismic data indicate that the structure of the wavelet network is much easier to be determined than that of the artificial neural network, while the convergence of wavelet network is faster than that of artificial neural network. In addition, the high and low frequency information of the adjacent channel can be transformed to the other seismic channel by taking the advantage of better similarity of subsection time-frequency analysis of seismic data, as well as the capability of learning, training and memorizing of the wavelet network. In this way, we may enhance not only the resolution capability but also the reliability of seismic data inversion.
In the light of the theoretical technique of wavelet networks, we study the method for inversing the time difference of artificial acoustic wave (Δt) by using multi-scale decompositon of wavelet from seismic records. We analyze also the theoretical method of wavelet decomposition, as well as network learning and training from seismic signals. In these studies, we find that for the adjacent seismic channels, the similarity of a smaller section is better than that of the whole channel. Therefore, the information obtained from the adjacent seismic channel can be extrapolated to the other channel by using time-frequency analysis method of wavelet. The results of the afore-mentioned studies, as well as the inversion and calculation of practical seismic data indicate that the structure of the wavelet network is much easier to be determined than that of the artificial neural network, while the convergence of wavelet network is faster than that of artificial neural network. In addition, the high and low frequency information of the adjacent channel can be transformed to the other seismic channel by taking the advantage of better similarity of subsection time-frequency analysis of seismic data, as well as the capability of learning, training and memorizing of the wavelet network. In this way, we may enhance not only the resolution capability but also the reliability of seismic data inversion.
引文
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张可村.1988.工程优化的算法与分析[M].西安:西安交通大学出版社.
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MiaoY,GantiA,LapidothA, etal.1998.FastsubspacetrackingandNNlearningbyanovelinformationcriterion[J].IEEETransSignalProcessing,46:1967—1979.
VeenAV,BlaumM,BrucknJ, etal.1993.Subspace basedsignalAanalysisusingSVD [J].ProcIEEE,81:1277—1308.
CAIDa yong,BAIFeng shan.2000.ModernScientificComputation[M].SciencePress,Beijing(inChinese).
陈遵德.1998.储层地震属性优化方法[M].北京:石油工业出版社.
CHENZun de.1998.TheOptimizationAlgorithmofSeismicPropertiesofOilReservoir[M].PublishingHouseofPetroleumIndustry,Beijing(inChinese).
程正兴.1998.小波分析算法与应用[M].西安:西安交通大学出版社.
CHENGZheng xing.1998.TheWaveletAnalysisAlgorithmanditsApplication[M].PublishingHouseofXi′anJiaotongUniversity,Xi′an(inChinese).
孙继广.1987.矩阵扰动分析[M].北京:科学出版社.
SUNJi guang.1987.TheAnalysisofMatrixDisturbance[M].SciencePress,Beijing(inChinese).
阎平凡,张长水.2000.人工神经网络与模拟进化计算[M].北京:清华大学出版社.
YANPing fan,ZHANGChang shui.2000.ArtificialNeuralNetworkandSimulatedEvolutionalAlgorithm[M].TsinghuaUniversityPress,Beijing(inChinese).
张可村.1988.工程优化的算法与分析[M].西安:西安交通大学出版社.
ZHANGKe cun.1988.TheAnalysisandAlgorithmofEngineeringOptimization[M].PublishingHouseofXi′anJiaotongUniversity,Xi′an(inChinese).
MiaoY,GantiA,LapidothA, etal.1998.FastsubspacetrackingandNNlearningbyanovelinformationcriterion[J].IEEETransSignalProcessing,46:1967—1979.
VeenAV,BlaumM,BrucknJ, etal.1993.Subspace basedsignalAanalysisusingSVD [J].ProcIEEE,81:1277—1308.
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