Premium
ZERO MEMORY NON‐LINEAR DECONVOLUTION *
Author(s) -
GODFREY R.,
ROCCA F.
Publication year - 1981
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/j.1365-2478.1981.tb00401.x
Subject(s) - deconvolution , seismogram , algorithm , reflection (computer programming) , gaussian , estimator , reflection coefficient , sequence (biology) , convergence (economics) , computer science , zero (linguistics) , blind deconvolution , series (stratigraphy) , iterative method , waveform , superposition principle , mathematics , geology , optics , physics , mathematical analysis , statistics , seismology , telecommunications , radar , philosophy , economic growth , linguistics , biology , genetics , paleontology , quantum mechanics , programming language , economics
A bstract A type of iterative deconvolution that extracts the source waveform and reflectivity from a seismogram through the use of zero memory, non‐linear estimators of reflection coefficient amplitnde is developed. Here, we present a theory for iterative deconvolution that is based upon the specification of a stochastic model describing reflectivity. The resulting parametric algorithm deconvolves the seismogram by forcing a filtered version of the seismogram to resemble an estimated reflection coefficient sequence. This latter time series is itself obtained from the filtered seismogram, and so a degree of iteration is required. Algorithms utilizing zero memory non‐linearities (ZNLs) converge to a family of processes, which we call Bussgang, of which any colored Gaussian process and any independent process are members. The direction of convergence is controlled by the choice of ZNL used in the algorithm. Synthetic and real data show that, generally, five to ten iterations are required for acceptable deconvolutions.