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Modern singular spectral-based denoi...

Tiwari, R. K.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modern singular spectral-based denoising and filtering techniques for 2D and 3D reflection seismic data/ by R. K. Tiwari, R. Rekapalli.
作者:	Tiwari, R. K.
其他作者:	Rekapalli, R.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xvi, 157 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Seismology - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-030-19304-1
ISBN:	9783030193041

Modern singular spectral-based denoising and filtering techniques for 2D and 3D reflection seismic data
Tiwari, R. K.

Modern singular spectral-based denoising and filtering techniques for 2D and 3D reflection seismic data[electronic resource] /by R. K. Tiwari, R. Rekapalli. - Cham :Springer International Publishing :2020. - xvi, 157 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783030193041

Standard No.: 10.1007/978-3-030-19304-1doiSubjects--Topical Terms:

1085846
Seismology
--Data processing.

LC Class. No.: QE539.2.D36 / T593 2020

Dewey Class. No.: 551.22

Modern singular spectral-based denoising and filtering techniques for 2D and 3D reflection seismic data
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505 0 $a 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.
520 $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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