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|a Modern singular spectral-based denoising and filtering techniques for 2D and 3D reflection seismic data /
|c R.K. Tiwari, R. Rekapalli
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|a Cham :
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|c ©2020
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|a Chapter 7: Denoising the 3D Seismic Data Using Multichannel Singular Spectrum Analysis
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|a Description based upon print version of record
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|a Includes bibliographical references and index
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|a Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- Chapter 1: Introduction to Denoising and Data Gap Filling of Seismic Reflection Data -- 1.1 Introduction -- 1.2 General Classification of Noise in Seismic Data -- 1.2.1 Random Noise -- 1.2.2 Coherent Noise -- 1.3 Noise Suppression Methods Used in the Seismic Data Processing -- 1.4 Data Gap Filling -- 1.5 Singular Spectrum Analysis -- 1.6 SSA Methods for Seismic Data -- 1.7 Skeleton of the Book -- Chapter 2: Time and Frequency Domain Eigen Image and Cadzow Noise Filtering of 2D Seismic Data -- 2.1 Introduction
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|a 2.2 Time Domain Eigen Image Processing -- 2.2.1 Example 1 -- 2.2.2 Example 2 -- 2.3 Frequency Domain Eigen Image Processing -- 2.3.1 Example 3 -- 2.4 Time and Frequency Domain Cadzow Filters -- 2.4.1 Pseudo Code of Time Domain Cadzow Filter -- 2.4.2 Pseudo Code of Frequency Domain Cadzow Filter -- 2.4.3 Example 4 -- 2.5 Conclusion -- Chapter 3: Singular Spectrum Analysis-Based Time Domain Frequency Filtering -- 3.1 Introduction -- 3.2 Methodology -- 3.3 Data Analysis -- 3.3.1 Example of Testing on Synthetic Data -- 3.3.2 Application to Reflection Field Data
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|a 3.4 Grouping from Weighted Eigen Spectrogram (WES) -- 3.5 Conclusion -- Chapter 4: Frequency and Time Domain SSA for 2D Seismic Data Denoising -- 4.1 Introduction -- 4.2 Methodological Description -- 4.2.1 FXSSA/Fxy Eigen Image Pseudo Code -- 4.2.2 TXSSA Pseudo Code -- 4.3 Example 1: F-xy Eigen Image Noise Suppression (Trickett 2003) -- 4.4 Example 2: Comparison of FXSSA Denoising with f-x Deconvolution (After Sacchi 2009) -- 4.5 Example 3: FXSSA Denoising of Synthetic Data in Comparison with TXSSA Method -- 4.6 Conclusion
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|a 6.2 Optimized SSA Method -- 6.2.1 Methodology -- 6.2.2 Coloured Noise Suppression Using Optimized SSA -- 6.3 Factorized Hankel SVD -- 6.3.1 Methodology -- 6.3.2 Testing on Synthetic Data -- 6.3.3 Low Frequency Preservation in Factorized Hankel Method -- 6.3.4 Computational Efficiency -- 6.3.5 Application of the Method to Post Stack Seismic Data -- 6.4 Randomized SVD (R-SVD) -- 6.4.1 Methodology/Algorithm -- 6.4.2 Application of R-SVD to Seismic Data -- 6.5 Windowed SSA -- 6.5.1 Methodology -- 6.5.2 Application to a Seismic Reflection Trace -- 6.6 Conclusion
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|a Chapter 5: Filtering 2D Seismic Data Using the Time Slice Singular Spectral Analysis -- 5.1 Introduction -- 5.2 Example of Crustal Stratification -- 5.3 Time Slice Singular Spectrum Analysis (TSSSA) Methodology -- 5.4 Selection of Window Length and Triplet Group -- 5.5 Application to Synthetic Data -- 5.6 Application of TSSSA and FXSSA on Pre and Post Stack Seismic Field Data -- 5.7 Application of the Method on Seismic Field Data from Singareni Coal Field, Telangana, India -- 5.8 Conclusion -- Chapter 6: Robust and Fast Algorithms for Singular Spectral Analysis of Seismic Data -- 6.1 Introduction
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|a This book discusses the latest advances in singular spectrum-based algorithms for seismic data processing, providing an update on recent developments in this field. Over the past few decades, researchers have extensively studied the application of the singular spectrum-based time and frequency domain eigen image methods, singular spectrum analysis (SSA) and multichannel SSA for various geophysical data. This book addresses seismic reflection signals, which represent the amalgamated signals of several unwanted signals/noises, such as ground roll, diffractions etc. Decomposition of such non-stationary and erratic field data is one of the multifaceted tasks in seismic data processing. This volume also includes comprehensive methodological and parametric descriptions, testing on appropriately generated synthetic data, as well as comparisons between time and frequency domain algorithms and their applications to the field data on 1D, 2D, 3D and 4D data sets. Lastly, it features an exclusive chapter with MATLAB coding for SSA
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|a Print version record
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| 650 |
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|a Microseisms
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|a Seismic reflection method
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|a Seismology
|x Data processing
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|a Spectrum analysis
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|a Méthode sismique-réflexion
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|a Sismologie
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| 650 |
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|a Seismic reflection method
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| 650 |
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|a Tiwari, R. K
|t Modern Singular Spectral-Based Denoising and Filtering Techniques for 2D and 3D Reflection Seismic Data
|d Cham : Springer,c2020
|z 9783030193034
|
| 776 |
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|a Tiwari, R.K
|t Modern Singular Spectral-Based Denoising and Filtering Techniques for 2D and 3D Reflection Seismic Data.
|d Cham : Springer, ©2020
|z 9783030193034
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