Deconvolution of nonstationary seismic data using adaptive lattice filters

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.