Interpolation of near offsets using multiples and prediction-error filters

Interpolation of near offsets using multiples and prediction-error filters

William Curry and Guojian Shan

Abstract:

Most conventional marine reflection seismic data lack sampling of near offsets. We address this problem by interpolating data with a nonstationary prediction-error filter (PEF) that is first estimated from fully-sampled training data and then is used to interpolate missing data to produce an interpolated output. These training data need not be perfect, and may differ in amplitude and phase but should contain the local multi-dimensional amplitude spectra of the data we wish to recreate. We generate pseudo-primary data by crosscorrelating multiples and primaries in the recorded data. These pseudo-primary data can be generated at missing near offsets, but contain many artifacts, so it is undesirable simply to replace the missing data with the pseudo-primaries. Fortunately, many of the problems with the pseudo-primaries do not influence PEF estimation, so a desirable PEF can be obtained from these data, and then used to interpolate the missing near inline offsets to produce a result that is superior to direct substitution of the pseudo-primaries into the missing offsets.

Interpolation of near offsets using multiples and prediction-error filters