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Author: Yangkang Chen
Publisher:
ISBN:
Category :
Languages : en
Pages : 332
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Book Description
Simultaneous shooting achieves a much faster seismic acquisition but poses a challenging problem for subsequent processing because of the interference from the neighbor crews. Separation of different sources, also called deblending, becomes important for the overall success of this acquisition technology. I propose a novel iterative estimation scheme for separating the blended simultaneous source seismic data to produce separate-source data as if they were acquired independently. I construct an augmented estimation problem, then use shaping regularization to constrain the characteristics of the model during the inversion and to obtain a suitable estimation result. The data reconstruction and source separation problems can be combined into one problem in order to make the future acquisition more flexible and efficient. In order to best utilize the capability of median filtering in attenuating spike-like noise, I also propose to use a new type of median filter (MF), termed as space-varying median filter (SVMF) to remove blending noise. SVMF can be regionally adaptive, instead of rigidly using a constant window length through the whole profile for MF. Simultaneous-source seismic data may also contain strong ambient random noise, so traditional denoising is still an important step. One of the most widely used approaches for removing random noise is using a sparse-transform thresholding strategy. I propose a double sparsity dictionary (DSD) for seismic data in order to combine the benefits of both analytic transform and learning-based dictionary. In the DSD framework, data-driven tight frame (DDTF) obtains an extra structure regularization when learning dictionaries, while the seislet transform obtains a compensation for the transformation error caused by slope dependency. DSD aims to provide a sparser representation than the individual transform and dictionary and therefore can help achieve better performance in denoising applications. Finally, considering that signal loss sometimes cannot be avoided in nearly all the existing denoising or deblending approaches. I propose a novel approach to retrieve the leakage energy from the initial noise section using local signal-and-noise orthogonalization. The proposed denoising approach corresponds to orthogonalizing the initially denoised signal and noise in a local manner. I evaluate denoising performance by using local similarity. The local signal-and-noise orthogonalization algorithm can also be used in the iterative deblending framework for obtaining better performance.
Author: Yangkang Chen
Publisher:
ISBN:
Category :
Languages : en
Pages : 332
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Book Description
Simultaneous shooting achieves a much faster seismic acquisition but poses a challenging problem for subsequent processing because of the interference from the neighbor crews. Separation of different sources, also called deblending, becomes important for the overall success of this acquisition technology. I propose a novel iterative estimation scheme for separating the blended simultaneous source seismic data to produce separate-source data as if they were acquired independently. I construct an augmented estimation problem, then use shaping regularization to constrain the characteristics of the model during the inversion and to obtain a suitable estimation result. The data reconstruction and source separation problems can be combined into one problem in order to make the future acquisition more flexible and efficient. In order to best utilize the capability of median filtering in attenuating spike-like noise, I also propose to use a new type of median filter (MF), termed as space-varying median filter (SVMF) to remove blending noise. SVMF can be regionally adaptive, instead of rigidly using a constant window length through the whole profile for MF. Simultaneous-source seismic data may also contain strong ambient random noise, so traditional denoising is still an important step. One of the most widely used approaches for removing random noise is using a sparse-transform thresholding strategy. I propose a double sparsity dictionary (DSD) for seismic data in order to combine the benefits of both analytic transform and learning-based dictionary. In the DSD framework, data-driven tight frame (DDTF) obtains an extra structure regularization when learning dictionaries, while the seislet transform obtains a compensation for the transformation error caused by slope dependency. DSD aims to provide a sparser representation than the individual transform and dictionary and therefore can help achieve better performance in denoising applications. Finally, considering that signal loss sometimes cannot be avoided in nearly all the existing denoising or deblending approaches. I propose a novel approach to retrieve the leakage energy from the initial noise section using local signal-and-noise orthogonalization. The proposed denoising approach corresponds to orthogonalizing the initially denoised signal and noise in a local manner. I evaluate denoising performance by using local similarity. The local signal-and-noise orthogonalization algorithm can also be used in the iterative deblending framework for obtaining better performance.
Author: Ray Abma
Publisher: SEG Books
ISBN: 1560803789
Category : Science
Languages : en
Pages : 220
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Book Description
This book introduces simultaneous source technology and helps those who practice it succeed. Although the book does not include all developments, which would have entailed a much longer treatise, this work is written through the lens of decades of experiences and allows readers to understand the development of independent simultaneous sourcing. The relationships between data acquisition and data processing are discussed because never before have they been so intertwined as in this area. In addition to describing the underlying technologies, this book also is a user-guide which discusses survey design and acquisition and decribes the sensitivities of the processing algorithms which can allow simultaneous source technology to succeed. The audience for this book includes acquisition and processing geophysicists who will work with these data as well as those who require only an overview of the state of the art; and, even though they may not need the full technical details, they may want to know the limitations and advantages of using simultaneous sources.
Author: Julien Meunier
Publisher: SEG Books
ISBN: 1560802812
Category : Science
Languages : en
Pages : 251
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Book Description
The rapid development of seismic acquisition, including wide-azimuth surveys, increased channel count, and simultaneous shooting, is made possible by technological advancements today that will enable the production of clearer seismic images tomorrow. The core of this book is the relationship between acquisition parameters and seismic image quality.
Author:
Publisher: Allied Publishers
ISBN: 9788177640410
Category :
Languages : en
Pages : 268
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Book Description
Author: Abdullatif Al-Shuhail
Publisher: Springer Nature
ISBN: 3030329488
Category : Technology & Engineering
Languages : en
Pages : 190
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Book Description
This book examines the effects of incoherent noise and how it leads to the misinterpretation of seismic data. It also reviews common noise reduction approaches and their drawbacks, focusing on developments that have occurred in the past decade. The main features of this book include: • Hands-on implementation in MATLAB and/or C • In-depth discussions of both theoretical and practical aspects of the subject • Supplementary, real-world seismic data • Detailed descriptions of structure-enhancing filters. Connecting the theory and practical implementation of noise reduction, the book helps readers fill the gap from equations to code, and from classical filters to the preservation and enhancement of a robust structure. Lastly, it highlights cutting-edge research in the area. As such, it is of interest to researchers in the fields of petroleum engineering, exploration seismology, and geophysics, as well as to practitioners working in the petroleum industry.
Author: David J. Monk
Publisher: SEG Books
ISBN: 1560803703
Category : Science
Languages : en
Pages : 215
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Book Description
Seismic surveys are subject to many different design criteria, but often the parameters are established based on an outdated view of how data can be acquired and how it will be processed. This book highlights what is possible using modern acquisition methods, techniques, and equipment, and how these may impact seismic survey design and acquisition.
Author: E. R. Kanasewich
Publisher: Pergamon
ISBN:
Category : Science
Languages : en
Pages : 252
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Book Description
This volume attempts to examine the sequence of operations required for the extraction of the flow of messages from a background of random noise and unwanted signals. The analysis will involve the use of frontier integrals, autocorrelation, cross correlation, power spectral studies and convolution or filtering. Attention will be made to the definition of signal and noise and how these may change under different processing methods.
Author: Rongzhi Lin
Publisher:
ISBN:
Category : Geophysics
Languages : en
Pages : 0
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Book Description
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.
Author: Antoine Guitton
Publisher:
ISBN:
Category : Seismic prospecting
Languages : en
Pages : 197
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Book Description
Author: Ekaterina Shipilova
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Simultaneous-source seismic data acquisition has recently attracted great attention both in the oil and gas industry and in academia, thanks to its capacity to save data acquisition time. Despite the evident time-saving advantage, the simultaneous-source method has a considerable draw-back: the sources interfere with each other creating cross-talk in the data, which leads to significant increase of the processing complexity and potential loss in the subsurface image quality. Recent advances in processing and imaging allow acceptable handling of the cross-talk, however, specific processing methods adapted for blended data still need to be improved. Many of the currently proposed separation methods need some preprocessing of the data, e.g., surface waves suppression. In this thesis, we propose to use a data-driven seismic event model in a greedy decomposition to obtain a separation suitable for raw data without any preprocessing. The proposed method is based on identifying coherent features in the data and classifying them according to their source of origin. We use two nested applications of Orthogonal Matching Pursuit, whose dictionaries are constituted of data-driven models of seismic events and wavelets. Thanks to several optimization steps and starting with appropriate initial conditions, we are able to effectively maximize a non-concave objective function and achieve a satisfactory separation quality, which we demonstrate on synthetic and real simultaneous-source signals.