Algorithms for sparse multichannel blind deconvolution

• Published in: IEEE transactions on geoscience and remote sensing Vol. 61; p. 1
• Main Authors: Nose-Filho, Kenji, Lopes, Renato, Brotto, Renan D. B., Senna, Thonia C., Romano, Joao M. T.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acquisition; Algorithms; Correlation coefficient; Correlation coefficients; Deconvolution; Estimation; Filtering algorithms; Finite impulse response filters; Inspection; Land acquisition; Maximum likelihood detection; Nonlinear filters; Prediction algorithms; Reflectance; Reflectivity; Seismic data; Signal to noise ratio; Simulation; Sparsity; Visual inspection
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3253387

In this paper we present two algorithms for sparse multichannel blind deconvolution. 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 SNR levels showed that the AM-SMBD was outperformed (in terms of the Pearson Correlation Coefficient and the Gini Correlation Coefficient) other estimation methods, like the reduced SMBD, the TS-sparseTLS (Toeplitz-structured sparse total least square) and the SMBD-SPG (Sparse multichannel blind deconvolution via spectral projected-gradient). For the same data, the C-PEF was able to provide better results (in terms of the Gini Correlation Coefficient, visual inspection and frequency gain) when compared with the F-SMBD (Fast sparse multichannel blind deconvolution). 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.