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Robust reconstruction of aliased data using autoregressive spectral estimates
Author(s) -
Naghizadeh Mostafa,
Sacchi Mauricio D.
Publication year - 2010
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.1365-2478.2010.00889.x
Subject(s) - autoregressive model , computer science , fourier transform , spectral density estimation , spectrum (functional analysis) , geology , algorithm , data mining , mathematics , statistics , physics , mathematical analysis , quantum mechanics
Autoregressive modeling is used to estimate the spectrum of aliased data. A region of spectral support is determined by identifying the location of peaks in the estimated spatial spectrum of the data. This information is used to pose a Fourier reconstruction problem that inverts for a few dominant wavenumbers that are required to model the data. Synthetic and real data examples are used to illustrate the method. In particular, we show that the proposed method can accurately reconstruct aliased data and data with gaps.