Modern singular spectral based de-noising and filtering techniques for 2D and 3D reflection seismic data

Subject ISBN Author Publisher Number of Pages Title Year Price
GEOLOGY AND EARTH SCIENCE 9789381891445 Rama Krishna Tiwari Capital Publishing Company 168 PP Modern singular spectral based de-noising and filtering techniques for 2D and 3D reflection seismic data 2018 Rs. 2695/-
Author: Rama Krishna Tiwari
Description: This book contains extensively studied singular spectrum based seismic data processing techniques. The algorithms in the book are related to the time and frequency domain of this seismic data processing. Most of the algorithms e.g. Eigen Image Processing., FXSSA, frequency domain MSSA are tested on theoretical/synthetic data and real time data. To overcome the problems in frequency domain methods, the book explains the development of Weighted Eigen spectrogram based SSA frequency filtering algorithm, time domain (Time Slice SSA) SSA and MSSA based de-noising algorithm for 2D and 3D seismic data respectively. The MSSA based horizon de-noising algorithms are also discussed in the book in a very simple way with lucid examples and applications on synthetic and real data.
Table of Content: Introduction to de-noising and Data gap filling of seismic reflection data Introduction General classification of noise in seismic data Random Noise Coherent Noise Noise suppression methods using in the Seismic data processing Data Gap filling Singular Spectrum Analysis SSA methods for Seismic data Skeleton of the book Time and frequency domain Eigen Image and Cadzow noise filtering of D Seismic Data Introduction Time Domain Eigen Image processing Frequency domain Eigen Image processing Time and Frequency Domain Cadzow filters Pseudo code of time domain Cadzow Filter Pseudo code of frequency domain Cadzow Filter Conclusion Time domain frequency filtering of High Resolution Seismic Reflection data using Singular Spectral Analysis Methodology Data analysis Testing on Synthetic data Application to high-resolution reflection field data from Singareni coalfield Grouping from Weighted Eigen Spectrogram (WES) Conclusion Frequency and Time domain SSA for D seismic data de-noising Introduction Methodological Description FXSSA/ Fxy Eigen Image Pseudo Code TXSSA Pseudo Code Example : F-xy Eigen Image noise suppression (Trickett, a) Example : Comparison of FXSSA de-noising with fx-deconvolution (After Sacchi, ) Example : FXSSA de-noising of Synthetic data in comparison with TXSSA method Conclusion Filtering D seismic data using the Time Slice Singular Spectral Analysis Introduction Example of crustal stratification Time slice Singular Spectrum Analysis (TSSSA) methodology Selection of Window length and Triplet group Application to synthetic data Application of TSSSA and FXSSA on pre and post stack seismic field data Application of the method on seismic field data from Singareni coal field, Telangana, India Conclusion Robust and Fast algorithms for Singular Spectral Analysis of Seismic data Introduction Optimal SSA Method Methodology Colored noise suppression using Optimized SSA Factorized Hankel SVD Methodology Testing on synthetic data Low frequency preservation in Factorized Hankel method Computational Efficiency Application of the method to post stack seismic data Randomized SVD (R-SVD) Methodology/Algorithm Application of R-SVD to seismic data Windowed SSA Methodology Application to a seismic reflection trace Conclusion Chapter De-noising the D seismic data using Multichannel Singular Spectrum Analysis Introduction Methodology Synthetic Examples Multichannel Time Slice SSA Frequency domain MSSA Application to Field Data Conclusion Seismic data gap filling using the Singular Spectrum based analysis Introduction Methodology Pseudo code Examples Frequency domain MSSA based d-data gap filling Time domain MSSA based iterative data gap filling Conclusions Assessment of Singular Spectrum and Wavelet based de-noising schemes in generalized inversion based seismic wavelet estimation Introduction Methodology of Generalized Inversion based Wavelet estimation Analysis and Results Conclusion References Appendix Eigen Decomposition- Singular Value Decomposition Introduction Methodology Examples of Eigen decomposition.

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