Algorithms for sparse multichannel blind deconvolution
Kenji Nose-Filho; Renato Lopes; Renan D. B. Brotto; Thonia C. Senna; João M. T. Romano
ARTIGO
Inglês
Agradecimentos: This work was supported in part by the São Paulo Research Foundation (FAPESP) (BI0S—Brazilian Institute of Data Science) under Grant 2019/20899-4 and Grant 2020/09838-0, in part by the Coordination for the Improvement of Higher Education Personnel under code 001, and in part by the...
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Agradecimentos: This work was supported in part by the São Paulo Research Foundation (FAPESP) (BI0S—Brazilian Institute of Data Science) under Grant 2019/20899-4 and Grant 2020/09838-0, in part by the Coordination for the Improvement of Higher Education Personnel under code 001, and in part by the National Council for Scientific and Technological Development (CNPq) under Grant 310824/2021-4
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Abstract: In this article, we present two algorithms for sparse multichannel blind deconvolution (SMBD). The first algorithm is based on a cascade of a forward and a backward prediction error filter (C-PEF). The second consists in an alternating minimization algorithm for estimating both the...
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Abstract: In this article, we present two algorithms for sparse multichannel blind deconvolution (SMBD). The first algorithm is based on a cascade of a forward and a backward prediction error filter (C-PEF). The second consists in an alternating minimization algorithm for estimating both the reflectivity series and the seismic wavelet (AM-SMBD). We also compare the algorithms with other state-of-the-art sparse blind deconvolution algorithms. Simulation results with synthetic data for different signal-to-noise ratio (SNR) levels showed that the AM-SMBD outperformed [in terms of the Pearson correlation coefficient (PCC) and the Gini correlation coefficient (GCC)] other estimation methods, such as the reduced SMBD, the Toeplitz-structured sparse total least square (TS-sparseTLS), and the SMBD via spectral projected gradient (SMBD-SPG). For the same data, the C-PEF was able to provide better results (in terms of the GCC, visual inspection, and frequency gain) when compared with the fast SMBD (F-SMBD). In a simulation considering reflectivities with different levels of sparsity, the C-PEF seems to be more robust for less sparse data when compared with AM-SMBD and SMBD-SPG (up to a certain degree of sparsity). Finally, simulations considering a real land acquisition show that both algorithms were able to greatly improve the resolution of the seismic data
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Resumo:
FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP
2019/20899-4; 2020/09838-0
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ
310824/2021-4
Fechado
DOI: https://doi.org/10.1109/tgrs.2023.3253387
Texto completo: https://ieeexplore.ieee.org/document/10061328
Algorithms for sparse multichannel blind deconvolution
Kenji Nose-Filho; Renato Lopes; Renan D. B. Brotto; Thonia C. Senna; João M. T. Romano
Algorithms for sparse multichannel blind deconvolution
Kenji Nose-Filho; Renato Lopes; Renan D. B. Brotto; Thonia C. Senna; João M. T. Romano
Fontes
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IEEE transactions on geoscience and remote sensing v. 61, n. art. 5905307, Mar. 2023 |