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Deconvolution of nonstationary seismic data using adaptive lattice filters

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3 Author(s)
Mahalanabis, A.K. ; Lehigh University, Bethelem, PA ; Prasad, S. ; Mohandas, K.P.

This paper examines the results of the application of two lattice algorithm to the problem of adaptive deconvolution on non-stationary seismic data. A comparative study of the deconvolution performance of the recently proposed gradient lattice and least-squares lattice algorithms is made with the help of experiments on simulated and real seismic data. We show that the gradient lattice algorithm is computationally superior, but it suffers from a possible slow rate of convergence, while the least-squares lattice has better convergence properties and is more robust numerically. We also show that both algorithms can yield equally good deconvolution results with a moderate amount of computation. Finally we indicate that a modified deconvolved output, derived as a linear combination of the forward and backward residuals, improves the performance without involving any additional computational burden.

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Acoustics, Speech and Signal Processing, IEEE Transactions on  (Volume:31 ,  Issue: 3 )