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

ISBN-13: 9783030193034 / Angielski / Twarda / 2020 / 157 str.

R.K. Tiwari; R. Rekapalli

Opis

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. The book discusses the latest advances in singular spectrum-based algorithms for seismic data processing, providing an update on recent developments. It 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.

Spis treści

1. Introduction to Denoising and Data Gap Filling of Seismic Reflection Data

2. Time and Frequency Domain Eigen Image and Cadzow Noise Filtering of 2D Seismic Data

3. Time Domain Frequency Filtering of High Resolution Seismic Reflection Data Using Singular Spectral Analysis

4. Frequency and Time Domain SSA for 2D Seismic Data Denoising

5. Filtering 2D Seismic Data Using the Time Slice Singular Spectral Analysis

6. Robust and Fast Algorithms for Singular Spectral Analysis of Seismic Data

7. Denoising the 3D Seismic Data Using Multichannel Singular Spectrum Analysis

8. Seismic Data Gap Filling Using the Singular Spectrum Based Analysis

9. Singular Spectrum vs. Wavelet Based Denoising Schemes in Generalized Inversion Based Seismic Wavelet Estimation

10. Singular Spectrum-based Filtering to Enhance the Resolution of Seismic Attributes

11. Singular Spectrum Analysis with MATLAB

Appendix

Index

O autorze

Dr. R.K. Tiwari, Chief Scientist (Retd.) is currently a Raja Ramanna Fellow at the National Geophysical Research Institute (NGRI), Hyderabad, India, where he is working on seismic curvature attribute studies. He is an expert on the theoretical modelling of non-linear geophysical processes, geophysical time series analysis, chaos and fractals. He has authored/co-authored more than 160 widely cited scientific papers in national and international journals. He has also published the book ‘Geospectroscopy’, which discusses the periodicities on multilateral scales in earth system processes. He served as the first coordinator of the Academy of Scientific and Innovative Research (AcSIR) CSIR-NGRI for PhD courses from 2011 to 2016. For his outstanding contributions in the field of earth sciences, he was awarded the prestigious IGU Krishnan Gold Medal (1987), National Mineral Award of Department of Mines, Government of India (2006) and the IGU Decennial Award (2009). He also received the Platinum Jubilee Lecture Award at the 95th Indian Science Congress (ISC).  He is a fellow of Indian Geophysical Union and a member of the Association of Exploration Geophysicists (AEG).

Dr. R. Rekapalli is a Senior Research Associate at the National Geophysical Research Institute, Hyderabad, India. As a theoretical geophysicist, he is working on the modelling of geophysical processes, geophysical signal processing as well as development and application of advanced methods for artefact-free geophysical data processing. He has developed several singular spectrum-based algorithms for 1D, 2D and 3D seismic data analysis. He received the Indian Geophysical Union’s Dr. J.G. Negi Young Scientist award (2018) in recognition to his research related to geophysical signal processing. He has authored/co-authored 26 papers in peer-reviewed national and international journals.

Blurb

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.