Modern Singular Spectral-Based Denoising and Filtering Techniques for 2D and 3D Reflection Seismic Data

✍ Scribed by R. K. Tiwari, R. Rekapalli

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 165
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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
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
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
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
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
Chapter 7: Denoising the 3D Seismic Data Using Multichannel Singular Spectrum Analysis
7.1 Introduction
7.2 Methodology
7.3 Synthetic Examples
7.3.1 Multichannel Time Slice SSA
7.3.2 Frequency Domain MSSA
7.4 Application to Field Data
7.5 Conclusion
Chapter 8: Seismic Data Gap Filling Using the Singular Spectrum Based Analysis
8.1 Introduction
8.2 Methodology
8.2.1 Pseudo Code
8.3 Examples
8.4 Frequency Domain MSSA Based 3D-Data Gap Filling
8.5 Time Domain MSSA Based Iterative Data Gap Filling
8.6 Conclusions
Chapter 9: Singular Spectrum vs. Wavelet Based Denoising Schemes in Generalized Inversion Based Seismic Wavelet Estimation
9.1 Introduction
9.2 Generalized Inversion Based Wavelet Estimation
9.3 Analysis and Results
9.4 Conclusion
Chapter 10: Singular Spectrum-Based Filtering to Enhance the Resolution of Seismic Attributes
10.1 Introduction
10.2 TSSSA-Based Filtering to Improve Post Stack 2D Attributes
10.2.1 Example 1: 2D Post Stack Attributes from TSSSA Filtered Data
10.3 MSSA-Based Pre-filtering of Horizon Time Structures and Amplitudes to Enhance the Resolution
10.3.1 Example 2: Synthetic Modeling
10.3.2 Example 3: Enhancing the Resolution of Curvature Attributes from Utsira Top (UT) Horizon
10.3.3 Example 4: MSSA-Based Pre-filtering of Horizon Amplitudes from 3D Volumes to Interpret the Physical Changes in Horizon
10.4 Conclusion
Chapter 11: Singular Spectrum Analysis with MATLAB®
11.1 Introduction
11.2 MATLAB® Coding: Description and Application
11.2.1 Signal Decomposition
11.2.2 Signal Reconstruction
11.2.3 Eigen Modes and their Separation
11.3 MATLAB® Function for Singular Spectrum Analysis of 1D Data Series
11.4 Application of SSA to High Resolution Seismic Trace Data
11.5 Summary
Appendix: Eigen Decomposition—Singular Value Decomposition
Introduction
Methodology
Examples of Eigen Decomposition
Singular Value Decomposition
Computational Steps Involved in SVD of Non-square Matrix A
References
Index