宽频带地震数据瞬时谱分解及快捷解释方法
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摘要
瞬时谱分解产生的若干共频率瞬时谱数据体不仅占用大量存储资源,而且影响了地震资料解释的效率。为此,本文首先提出联合应用短时傅里叶变换及S变换的时频谱分解方法,提高中、低频段时间分辨率及分解宽频带地震数据;接着应用分频段主成分分析实现宽频带瞬时谱数据集的降维与优化,并保持不同频段信息随频率变化的特性;最后将聚类测度引入模糊C均值聚类中,对经分频段主成分分析后数据集做进一步自适应融合。实际资料应用效果证实,本文方法可有效分解宽频带地震数据,快捷地提取和突出包含在大量瞬时谱数据集中的主要信息,清晰地刻画储层几何形态和展布特征,节省了存储资源并提高了宽频带地震资料解释效率。
Several iso-frequency instantaneous spectral data volumes generated from spectral decomposition of broadband seismic data(BBSD)not only occupy agreat deal of storage resource,but also make the interpretation become huge workload.In order to overcome these problems,we propose a new solution.First we introduce a new BBSD spectral decomposition based on Fourier transform and generalized S transform(GST),which can improve time resolution of time-frequency spectra at low frequency.Then frequency band principal components analysis(DFBPCA)is proposed to implement the dimensionality reduction and optimization of broadband instantaneous data sets,which can maintain information characteristics with frequency.Finally a cluster separation measure is introduced into the fuzzy C-means clustering(FCM)to implement further adaptive fusion of data sets from DFBPCA.According to real data application,the proposed method decomposes BBSD into isofrequency instantaneous spectral data sets with good time-frequency resolution,extracts fast and highlights the primary information contained in a large number of instantaneous spectral data sets,and depicts spatial distribution characteristics of reservoirs while reducing storage resource and improving BBSD interpretation.
Several iso-frequency instantaneous spectral data volumes generated from spectral decomposition of broadband seismic data(BBSD)not only occupy agreat deal of storage resource,but also make the interpretation become huge workload.In order to overcome these problems,we propose a new solution.First we introduce a new BBSD spectral decomposition based on Fourier transform and generalized S transform(GST),which can improve time resolution of time-frequency spectra at low frequency.Then frequency band principal components analysis(DFBPCA)is proposed to implement the dimensionality reduction and optimization of broadband instantaneous data sets,which can maintain information characteristics with frequency.Finally a cluster separation measure is introduced into the fuzzy C-means clustering(FCM)to implement further adaptive fusion of data sets from DFBPCA.According to real data application,the proposed method decomposes BBSD into isofrequency instantaneous spectral data sets with good time-frequency resolution,extracts fast and highlights the primary information contained in a large number of instantaneous spectral data sets,and depicts spatial distribution characteristics of reservoirs while reducing storage resource and improving BBSD interpretation.
引文
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[18]万忠宏,詹仕凡,曹永忠.基于几何学和动力学特征分析的三维属性体研究.四川成都:SEG北京联络部2007年第三季度学术交流会,2007,19-28.Wan Zhonghong,Zhan Shifan,Cao Yongzhong.Study on 3Dattributes cube based on seismic geometry and kinetics characterization analysis.SEG Beijing Liaison Department in the third quarter of 2007Symposium,Chengdu,China,19-28.
[19]Chiu S K.Coherent and random noise attenuation via multichannel singular spectrum analysis in the randomized domain.Geophysical Prospecting,2013,61(S1):1-9.
[2]Liu J,Marfurt K J.Instantaneous spectral attributes to detect channel.Geophysics,2007,72(2):P23-P31.
[3]Guo H,Marfurt K J.Principal components analysisof spectral components.SEG Technical Program Expanded Abstracts,2006,25:988-992.
[4]Guo H,Lewis S,Marfurt K J.Mapping multiple attributes to three and four-component color models-a tutorial.Geophysics,2008,73(3):W7-W19.
[5]陈学华,贺振华,钟文丽等.基于主成分分析的自适应宽频带瞬时谱数据融合.石油地球物理勘探,2011,46(4):576-580.Chen Xuehua,He Zhenhua,Zhong Wenli et al.Broad frequency band instantaneous spectral auto adaptive data fusion based on principal components analysis.OGP,2011,46(4):576-580.
[6]Deborah F,Kasper V W,Rutledge J.Clustering revisited:A spectral analysis of microseismic events.Geophysics,2013,78(2):KS41-KS48.
[7]Stelios k,Vassilios C.A robust fuzzy local information C-means clustering algorithm.IEEE Transactions on Image Processing,2010,19(5):1328-1338.
[8]Carvalho F.Fuzzy C-means clustering methods for symbolic interval data.Pattern Recognition Letters,2007,28(4):423-437.
[9]Thitimajshima P.A new modified fuzzy C-means algorithm for multispectral satellite images segmentation.Proceeding of Geoscience and Remote Sensing Symposium(IGARSS),2000,4:684-1686.
[10]Tamalika C.A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images.Applied Soft Computing,2011,11(2):1711-1717.
[11]Goloshubin G M,Connie V A,Korneev V A et al.Reservoir imaging using low frequencies of seismic reflections.The Leading Edge,2006,25(5):527-531.
[12]Stockwell R G,Mansinha L,Lowe R P.Localization of the complex spectrum:the S transform.IEEE Transactions on Signal Processing,1996,44(4):998-1001.
[13]陈学华,贺振华,黄德济.基于广义S变换的地震资料高效时频谱分解.石油地球物理勘探,2008,43(5):530-534.Chen Xuehua,He Zhenhua,Huang Deji.High-efficient time-frequency spectrum decomposition of seismic data based on generalized S transform.OGP,2008,43(5):530-534.
[14]Dunn J C.A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters.Journal of Cybernetics,1974,3(3):32-57.
[15]Bezdek J C.Pattern Recognition with Fuzzy Objective Function Algorithms.New York:Plenum Press,1981.
[16]Davies D L,Bouldin D W.A cluster separation measure.IEEE Transactions on Pattern Analysis and Machine Intelligence,1979,PAMI-1(2):224-227.
[17]贺振华,黄德济.挑战极值频率——论截频或限频以外地震信息的利用.中国地球物理学会第22届年会论文集,四川成都:四川科学技术出版社,2006.He Zhenhua,Huang Deji.Challenging extreme frequency-discussing seismic information out of cut off frequency.22nd Annual Meeting Symposium of Chinese Geophysical Society.Chengdu:Press of Sichuan Science,2006.
[18]万忠宏,詹仕凡,曹永忠.基于几何学和动力学特征分析的三维属性体研究.四川成都:SEG北京联络部2007年第三季度学术交流会,2007,19-28.Wan Zhonghong,Zhan Shifan,Cao Yongzhong.Study on 3Dattributes cube based on seismic geometry and kinetics characterization analysis.SEG Beijing Liaison Department in the third quarter of 2007Symposium,Chengdu,China,19-28.
[19]Chiu S K.Coherent and random noise attenuation via multichannel singular spectrum analysis in the randomized domain.Geophysical Prospecting,2013,61(S1):1-9.
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