A comparative study over improved fast iterative shrinkage-thresholding algorithms: an application to seismic data reconstruction

Seismic data reconstruction is a crucial process involving the restoration of missing or corrupted traces to create a uniform dataset for subsequent data processing. Various factors such as equipment failures, and surface obstacles, result in irregularly located or corrupted traces. The absence of t...

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Vydané v:Studia geophysica et geodaetica Ročník 68; číslo 1-2; s. 61 - 77
Hlavní autori: Khatami, Hamid Reza, Riahi, Mohammad Ali, Abedi, Mohammad Mahdi, Dehkhargani, Afshin Akbari
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.04.2024
Springer Nature B.V
Predmet:
Accuracy
Algorithms
Atmospheric Sciences
Comparative analysis
Comparative studies
Convergence
Data analysis
Data processing
Data recovery
Datasets
Earth and Environmental Science
Earth Sciences
Fast Fourier transformations
Fourier transforms
Geophysics/Geodesy
Image quality
Image reconstruction
Interpolation
Kernel functions
Missing data
Sampled data
Seismic data
Seismological data
Structural Geology
Synthetic data
FISTA
missing traces
data reconstruction
NUFFT
ISSN:0039-3169, 1573-1626
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Popis
Shrnutí:Seismic data reconstruction is a crucial process involving the restoration of missing or corrupted traces to create a uniform dataset for subsequent data processing. Various factors such as equipment failures, and surface obstacles, result in irregularly located or corrupted traces. The absence of these traces can compromise the quality and accuracy of the resulting image. To address this issue, the Nonuniform Fast Fourier Transform (NUFFT) method is employed to reconstruct missing traces in datasets with non-uniformly sampled data. It works by interpolating the non-uniformly sampled data onto a regular grid, enabling the traditional Fast Fourier Transform application for data recovery. This interpolation process is adjusted using a kernel function to account for non-uniform sampling and reduce aliasing artifacts. The outcome is a collection of Fourier coefficients that can be utilized to reconstruct missing or incomplete parts of data. This problem is transformed into a linear constraint problem, which is efficiently solved using the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). In this study, we explore various techniques aimed at improving the convergence of FISTA, collectively referred to as improved FISTA methods. To validate the NUFFT+FISTA method for data reconstruction, we conducted numerical tests using 3D and 2D synthetic datasets, as well as field data. These tests show the advantages of the Greedy-FISTA in terms of convergence rate and affirm the accuracy of this approach in filling missing data traces.
Bibliografia:ObjectType-Article-1
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ISSN:0039-3169
1573-1626
DOI:10.1007/s11200-023-0113-0