Recent laboratory measurement studies have suggested a growing consensus that fluid saturated rocks can have frequency-dependent properties within the seismic bandwidth. It is appealing to try to use these properties for the discrimination of fluid saturation from seismic data. In this paper, we develop a frequency-dependent AVO (FAVO) attribute to measure magnitude of dispersion from pre-stack data. The scheme essentially extends the Smith and Gidlow (1987)’s two-term AVO approximation to be frequency-dependent, and then linearize the frequency-dependent approximation with Taylor series expansion. The magnitude of dispersion can be estimated with least-square inversion. A high-resolution spectral decomposition method is of vital importance during the implementation of the FAVO attribute calculation. We discuss the resolution of three typical spectral decomposition techniques: the short term Fourier transform (STFT), continuous wavelet transform (CWT) and Wigner-Vill Distribution (WVD) based methods. The smoothed pseudo Wigner-Ville Distribution (SPWVD) method, which uses smooth windows in time and frequency domain to suppress cross-terms, provides higher resolution than that of STFT and CWT. We use SPWVD in the FAVO attribute to calculate the frequency-dependent spectral amplitudes from pre-stack data. We test our attribute on forward models with different time scales and crack densities to understand wave-scatter induced dispersion at the interface between an elastic shale and a dispersive sandstone. The FAVO attribute can determine the maximum magnitude of P-wave dispersion for dispersive partial gas saturation case; higher crack density gives rise to stronger magnitude of P-wave dispersion. Finally, the FAVO attribute was applied to real seismic data from the North Sea. The result suggests the potential of this method for detection of seismic dispersion due to fluid saturation.
{"title":"Estimating seismic dispersion from prestack data using frequency-dependent AVO analysis","authors":"Xiaoyang Wu, M. Chapman, Xiang-Yang Li","doi":"10.1190/1.3513759","DOIUrl":"https://doi.org/10.1190/1.3513759","url":null,"abstract":"Recent laboratory measurement studies have suggested a growing consensus that fluid saturated rocks can have frequency-dependent properties within the seismic bandwidth. It is appealing to try to use these properties for the discrimination of fluid saturation from seismic data. In this paper, we develop a frequency-dependent AVO (FAVO) attribute to measure magnitude of dispersion from pre-stack data. The scheme essentially extends the Smith and Gidlow (1987)’s two-term AVO approximation to be frequency-dependent, and then linearize the frequency-dependent approximation with Taylor series expansion. The magnitude of dispersion can be estimated with least-square inversion. A high-resolution spectral decomposition method is of vital importance during the implementation of the FAVO attribute calculation. We discuss the resolution of three typical spectral decomposition techniques: the short term Fourier transform (STFT), continuous wavelet transform (CWT) and Wigner-Vill Distribution (WVD) based methods. The smoothed pseudo Wigner-Ville Distribution (SPWVD) method, which uses smooth windows in time and frequency domain to suppress cross-terms, provides higher resolution than that of STFT and CWT. We use SPWVD in the FAVO attribute to calculate the frequency-dependent spectral amplitudes from pre-stack data. We test our attribute on forward models with different time scales and crack densities to understand wave-scatter induced dispersion at the interface between an elastic shale and a dispersive sandstone. The FAVO attribute can determine the maximum magnitude of P-wave dispersion for dispersive partial gas saturation case; higher crack density gives rise to stronger magnitude of P-wave dispersion. Finally, the FAVO attribute was applied to real seismic data from the North Sea. The result suggests the potential of this method for detection of seismic dispersion due to fluid saturation.","PeriodicalId":50054,"journal":{"name":"Journal of Seismic Exploration","volume":"81 1","pages":"219-239"},"PeriodicalIF":0.5,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1190/1.3513759","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65995583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Predictive deconvolution by frequency domain Wiener filtering","authors":"M. Broadhead, C. Liner, T. Ulrych","doi":"10.1190/1.2792997","DOIUrl":"https://doi.org/10.1190/1.2792997","url":null,"abstract":"","PeriodicalId":50054,"journal":{"name":"Journal of Seismic Exploration","volume":"1 1","pages":""},"PeriodicalIF":0.5,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1190/1.2792997","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65926124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Summary Classification algorithms have many applications both in exploration and production seismology. Many classification algorithms have been reported in the literature, such as, seismic facies identification, lithology/fluid prediction, etc. However, improper choice of an algorithm and parameters for a specific problem will create incorrect classification results. Here, we elaborate on some of these issues. Further, we propose combing multiple classifiers with DempsterShafer theory (DS) to increase the accuracy of the classification. The philosophy of our approach is that different classifiers may offer complementary information about the patterns to be classified, combining classifiers in an efficient way can achieve better classification results than any single classifier. The effectiveness of this method is demonstrated with a synthetic data test.
{"title":"Fusion Based Classification Method And Its Application","authors":"Long Jin, Mrinal K. Sen, P. Stoffa","doi":"10.1190/1.2792784","DOIUrl":"https://doi.org/10.1190/1.2792784","url":null,"abstract":"Summary Classification algorithms have many applications both in exploration and production seismology. Many classification algorithms have been reported in the literature, such as, seismic facies identification, lithology/fluid prediction, etc. However, improper choice of an algorithm and parameters for a specific problem will create incorrect classification results. Here, we elaborate on some of these issues. Further, we propose combing multiple classifiers with DempsterShafer theory (DS) to increase the accuracy of the classification. The philosophy of our approach is that different classifiers may offer complementary information about the patterns to be classified, combining classifiers in an efficient way can achieve better classification results than any single classifier. The effectiveness of this method is demonstrated with a synthetic data test.","PeriodicalId":50054,"journal":{"name":"Journal of Seismic Exploration","volume":"18 1","pages":"103-117"},"PeriodicalIF":0.5,"publicationDate":"2007-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65925727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Multiple Attenuation In the Plane Wave Domain By Match Filtering","authors":"R. Pestana, P. Stoffa, Mrinal K. Sen","doi":"10.1190/1.1820807","DOIUrl":"https://doi.org/10.1190/1.1820807","url":null,"abstract":"","PeriodicalId":50054,"journal":{"name":"Journal of Seismic Exploration","volume":"8 1","pages":"167-179"},"PeriodicalIF":0.5,"publicationDate":"1999-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65853100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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