Modern Singular Spectral Based Denoising And Filtering Techniques For 2d And 3d Reflection Seismic Data
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Modern Singular Spectral Based Denoising And Filtering Techniques For 2d And 3d Reflection Seismic Data
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Author : R. K. Tiwari
language : en
Publisher: Springer Nature
Release Date : 2020-03-25
Modern Singular Spectral Based Denoising And Filtering Techniques For 2d And 3d Reflection Seismic Data written by R. K. Tiwari and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-25 with Science categories.
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.
Tensor Computation For Seismic Data Processing
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Author : Feng Qian
language : en
Publisher: Springer Nature
Release Date : 2025-04-26
Tensor Computation For Seismic Data Processing written by Feng Qian and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-26 with Science categories.
This book aims to provide a comprehensive understanding of tensor computation and its applications in seismic data analysis, exclusively catering to seasoned researchers, graduate students, and industrial engineers alike. Tensor emerges as a natural representation of multi-dimensional modern seismic data, and tensor computation can help prevent possible harm to the multi-dimensional geological structure of the subsurface that occurred in classical seismic data analysis. It delivers a wealth of theoretical, computational, technical, and experimental details, presenting an engineer's perspective on tensor computation and an extensive investigation of tensor-based seismic data analysis techniques. Embark on a transformative exploration of seismic data processing—unlock the potential of tensor computation and reshape your approach to high-dimensional geological structures. The discussion begins with foundational chapters, providing a solid background in both seismic data processing and tensor computation. The heart of the book lies in its seven chapters on tensor-based seismic data analysis methods. From structured low-tubal-rank tensor completion to cutting-edge techniques like tensor deep learning and tensor convolutional neural networks, each method is meticulously detailed. The superiority of tensor-based data analysis methods over traditional matrix-based data analysis approaches is substantiated through synthetic and real field examples, showcasing their prowess in handling high-dimensional modern seismic data. Notable chapters delve into seismic noise suppression, seismic data interpolation, and seismic data super-resolution using advanced tensor models. The final chapter provides a cohesive summary of the conclusion and future research directions, ensuring readers facilitate a thorough understanding of tensor computation applications in seismic data processing. The appendix includes a hatful of information on existing tensor computation software, enhancing the book's practical utility.
The Singular Spectrum Analysis Method And Its Application To Seismic Data Denoising And Reconstruction
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Author : Vicente E. Oropeza
language : en
Publisher:
Release Date : 2010
The Singular Spectrum Analysis Method And Its Application To Seismic Data Denoising And Reconstruction written by Vicente E. Oropeza and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Seismology categories.
Multiple Suppression From 2 D Shallow Marine Seismic Reflection Data Using Filtering And Deconvolution Approaches
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Author :
language : en
Publisher:
Release Date : 2017
Multiple Suppression From 2 D Shallow Marine Seismic Reflection Data Using Filtering And Deconvolution Approaches written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Abstract : A primary objective of the seismic data processing workflow is to improve the signal to noise ratio. A seismic record has many types of noise besides primary reflections which convey the vital information. A non-negligible part of these noises is multiple reflections causing difficulties and misunderstandings. This work examines filtering techniques with different methods and deconvolution technique in an effort to attenuate multiples on a 2D line of marine data from southwest of the Taiwan and compares of their results. Prior to evaluating methods for attenuating multiples, basic seismic processing was applied to the data. This consisted of the following: zeroing bad traces, applying a spherical divergence correction, and band-pass filtering. The data were then sorted into common-mid-point (CMP) gathers. These CMP gathers were analyzed, and stacking velocities were determined so that Normal Move-out (NMO) processing and stacking can be applied. Following this basic processing, two methods of multiple suppression were applied separately and evaluated: 1) filtering; 2) deconvolution. The filtering methods included stacking, frequency(f)-wavenumber(k) filtering and the Radon Transform methods were applied in an effort to separate multiples and primaries. Deconvolution was also utilized. Finally, the results of these approaches were discussed and compared with the goal of obtaining reasonable results. For this data set, it appears that the Radon Transform attenuates the long-period multiples better than the other approaches. Applying deconvolution on Radon-filtered data also shows better results. Stacked and migrated section of the data was considered as the final image.
Spectral Decomposition Using S Transform For Hydrocarbon Detection And Filtering
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Author : Zhao Zhang
language : en
Publisher:
Release Date : 2012
Spectral Decomposition Using S Transform For Hydrocarbon Detection And Filtering written by Zhao Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Spectral decomposition is a modern tool that utilizes seismic data to generate additional useful information in seismic exploration for hydrocarbon detection, lithology identification, stratigraphic interpretation, filtering and others. Different spectral decomposition methods with applications to seismic data were reported and investigated in past years. Many methods usually do not consider the non-stationary features of seismic data and, therefore, are not likely to give satisfactory results. S-transform developed in recent years is able to provide time-dependent frequency analysis while maintaining a direct relationship with the Fourier spectrum, a unique property that other methods of spectral decomposition may not have. In this thesis, I investigated the feasibility and efficiency of using S-transform for hydrocarbon detection and time-varying surface wave filtering. S-transform was first applied to two seismic data sets from a clastic reservoir in the North Sea and a deep carbonate reservoir in the Sichuan Basin, China. Results from both cases demonstrated that S-transform decomposition technique can detect hydrocarbon zones effectively and helps to build the relationships between lithology changes and high frequency variation and between hydrocarbon occurrence and low-frequency anomaly. However, its time resolution needs to be improved. In the second part of my thesis, I used S-transform to develop a novel Time-frequency-wave-number-domain (T-F-K) filtering method to separate surface wave from reflected waves in seismic records. The S-T-F-K filtering proposed here can be used to analyze surface waves on separate f-k panels at different times. The method was tested using hydrophone records of four-component seismic data acquired in the shallow-water Persian Gulf where the average water depth is about 10m and Scholte waves and other surfaces wave persistently strong. Results showed that this new S-T-F-K method is able to separate and sttenuate surface waves and to improve greatly the quality of seismic reflection signals that are otherwise completely concealed by the aliased surface waves.
Seismic Filtering
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Author : Institut français du pétrole
language : en
Publisher: SEG Books
Release Date : 1971
Seismic Filtering written by Institut français du pétrole and has been published by SEG Books this book supported file pdf, txt, epub, kindle and other format this book has been release on 1971 with Science categories.
Sponsored by the French Petroleum Institute and of value to all seismic data processors, this tutorial symposium on seismic filtering contains seven chapters, each by a different author. Topics covered include introductions to 1D and 2D spectra, the FPI's delay-line filter, optical correlation, inverse filtering of plane waves, marine analog filtering, and seismic emission by vibrators.
Projected Gradient Descent Methods For Simultaneous Source Seismic Data Processing
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Author : Rongzhi Lin
language : en
Publisher:
Release Date : 2022
Projected Gradient Descent Methods For Simultaneous Source Seismic Data Processing written by Rongzhi Lin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Geophysics categories.
Simultaneous-source acquisition is a seismic data acquisition technology that has become quite popular in recent years due to its economic advantages. Contrary to the conventional seismic acquisition, where one records the seismic response of only one source at a time, in simultaneous source acquisition, an array of receivers record the response of more than one source. The latter leads to a saving in acquisition time, but it creates new problems in subsequent data processing stages where each seismic record must correspond to the response of one single source. The basic idea for simultaneous source data processing is to separate the sources and thereby estimate the responses one would have acquired via a conventional seismic data acquisition. Then one can adopt a traditional seismic workflow to process and invert the seismic data. This thesis focuses on developing inversion schemes for separating simultaneous-source data. I pay particular attention to strategies based on the Projected Gradient Descent (PGD) method with a projection synthesized via robust denoising algorithms. First, I propose adopting a robust and sparse Radon transform to define a coherence pass projection operator to guarantee solutions that honour simultaneous source records. I show that a critical improvement in convergence is attainable when the coherence pass projection originates from a robust and sparse Radon transform. The latter is a consequence of having an iterative source separation algorithm that applies intense denoising to erratic blending noise in its initial iterations. In addition, I also propose an inversion scheme for simultaneous-source data separation based on a robust low-rank approximation algorithm. A robust Multichannel Singular Spectrum Analysis (MSSA) filtering is adopted as the projection operator to suppress source interferences in the frequency-space domain. The MSSA method is reformulated as a robust optimization problem that includes a low-rank Hankel matrix constraint, written as the product of two matrices of lower dimension obtained by the bifactored gradient descent (BFGD) method. In the second part of my thesis, I explore an inversion scheme for source separation and source reconstruction that honours actual source coordinates. The proposed method adopts a projected gradient descent optimization with a reduced-rank MSSA projection operator. I propose to adopt an Interpolated-MSSA (I-MSSA) to separate and reconstruct sources in situations where the acquired simultaneous source data correspond to sources with ar- arbitrary irregular-grid coordinates. Additionally, a faster and computational-efficient MSSA (FMSSA) algorithm was applied to speed up the method.