A new generalized least mean-square algorithm for processing non-stationary seismic data

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Published: Jul 1, 1987
DOI: https://doi.org/10.22201/igeof.00167169p.1987.26.3.1312
Keywords:
Seismology, seismograms, algorithms, least mean square error technique, non-stationary earthquakes

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A. H. Cominguez
Instituto del Petróleo, Facultad de Ingeniería, Universidad de Buenos Aires

Abstract

An adaptive deconvolution algorithm based upon a generalized expression of the Least Mean-Square (LMS) error technique is presented. The use of this process is recommended for reflection seismic data which contain timevarying reverberations. Filter coefficients are designed for each sample of the input trace using the proposed method. Convergence characteristics of the new algorithm, and its stability properties, are analyzed and compared to the simple LMS algorithm. Illustrations using synthetic seismic data are presented. Future possibilities of application to real shallow-water seismic data are found promising.

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Cominguez, A. H. (1987). A new generalized least mean-square algorithm for processing non-stationary seismic data. Geofisica Internacional, 26(3), 393–406. https://doi.org/10.22201/igeof.00167169p.1987.26.3.1312
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Vol. 26 No. 3 (1987): July 1, 1987
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References

GRIFFITHS, T. L., 1969. A simple adaptive algorithm for real-time processing in antenna arrays. Proc. IEEE, 57, 1696-1704. DOI: https://doi.org/10.1109/PROC.1969.7385

GRIFFITHS, T. L., F. R. SMOLKA and L. D. TREMBLY, 1977. Adaptive deconvolution: a new technique for time-varying seismic data. Geophysics, 42, 742-759. DOI: https://doi.org/10.1190/1.1440743

LEE, Y. W., 1960. Statistical theory of communication. New York, John Wiley and Sons.

MANTEY, P. E. and L. T. GRIFFITHS, 1969. Iterative least-squares algorithm for signal extraction. Proc. Second Hawaii Int. Conf. System Sciences, Western Periodicals Co., 767-770.

MUELLER, K. H., 1975. A new fast-converging mean-square algorithm for adaptive equalizers with partial-response signaling. Bell. Syst. Tech. J., 54, 143-153. DOI: https://doi.org/10.1002/j.1538-7305.1975.tb02829.x

PEACOCK, K. L. and S. TREITEL, 1969. Predictive deconvolution. Theory and Practice. Geophysics, 34, 155-169. DOI: https://doi.org/10.1190/1.1440003

TREITEL, S. and E. A. ROBINSON, 1966. The design of high-resolution digital filters. IEEE Trans. on Geosci. Electronics, GE-4, 25-38. DOI: https://doi.org/10.1109/TGE.1966.271203

WIDROW, B. and M. E. HOFF, 1960. Adaptive switching circuits. WESCON Conv. Rec., p. t. 4, 96-140. DOI: https://doi.org/10.21236/AD0241531

WIDROW, B., T. M. McCOOL, M.G. LARIMORE and C. R. JOHNSON, 1976. Stationary and nonstationary learning characteristics of the LMS adaptive filter. Proc. IEEE, 64, 1151-1162. DOI: https://doi.org/10.1109/PROC.1976.10286