Projected Gradient Descent Methods For Simultaneous Source Seismic Data Processing
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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.
Scientific And Technical Aerospace Reports
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Author :
language : en
Publisher:
Release Date : 1992
Scientific And Technical Aerospace Reports written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Aeronautics categories.
Deep Learning For Seismic Data Enhancement And Representation
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Author : Shirui Wang
language : en
Publisher: Springer Nature
Release Date : 2024-12-18
Deep Learning For Seismic Data Enhancement And Representation written by Shirui Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-18 with Science categories.
Seismic imaging is a key component of subsurface exploration, and it depends on a high-quality seismic data acquisition system with effective seismic processing algorithms. Seismic data quality concerns various factors such as acquisition design, environmental constraints, sampling resolution, and noises. The focus of this book is to investigate efficient seismic data representation and signal enhancement solutions by leveraging the powerful feature engineering capability of deep learning. The book delves into seismic data representation and enhancement issues, ranging from seismic acquisition design to subsequent quality improvement and compression technologies. Given the challenges of obtaining suitable labeled training datasets for seismic data processing problems, we concentrate on exploring deep learning approaches that eliminate the need for labels. We combined novel deep learning techniques with conventional seismic data processing methods, and construct networks and frameworks tailored for seismic data processing. The editors and authors of this book come from both academia and industry with hands-on experiences in seismic data processing and imaging.
Noise Attenuation Of Seismic Data From Simultaneous Source Acquisition
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Author : Yangkang Chen
language : en
Publisher:
Release Date : 2015
Noise Attenuation Of Seismic Data From Simultaneous Source Acquisition written by Yangkang Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
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.