2007/01/01 by Mostafa Naghizadeh, Mauricio D. Sacchi · 2 citations
Earth and Planetary Sciences · Engineering · #Seismic Imaging and Inversion Techniques #Seismic Waves and Analysis #Hydraulic Fracturing and Reservoir Analysis
paper · doi:10.1190/1.2771685
Abstract Linear prediction filters in the f-x domain are widely used to interpolate regularly sampled data. We study the problem of reconstructing irregularly missing data on a regular grid using linear prediction filters. We propose a two-stage algorithm. First, we reconstruct the unaliased part of the data spectrum using a Fourier method (minimum-weighted norm interpolation). Then, prediction filters for all the frequencies are extracted from the reconstructed low frequencies. The latter is implemented via a multistep autoregressive (MSAR) algorithm. Finally, these prediction filters are used to reconstruct the complete data in the f-x domain. The applicability of the proposed method is examined using synthetic and field data examples.