Surface-related multiple elimination (SRME) is an algo-rithm that predicts all surface multiples by a convolutional process applied to seismic field data. Only minimal prepro-cessing is required. Once predicted, the multiples are re-moved from the data by adaptive subtraction. Unlike other methods of multiple attenuation, SRME does not rely on as-sumptions or knowledge about the subsurface, nor does it use event properties to discriminate between multiples and pri-maries. In exchange for this "freedom from the subsurface," SRME requires knowledge of the acquisition wavelet and a dense spatial distribution of sources and receivers. Although a 2D version of SRME sometimes suffices, most field data sets require 3D SRME for accurate multiple prediction. All implementations of 3D SRME face a serious challenge: The sparse spatial distribution of sources and receivers available in typical seismic field data sets does not conform to the algo-rithmic requirements. There are several approaches to imple-menting 3D SRME that address the data sparseness problem. Among those approaches are pre-SRME data interpolation, on-the-fly data interpolation, zero-azimuth SRME, and true-azimuth SRME. Field data examples confirm that (1) multi-ples predicted using true-azimuth 3D SRME are more accu-rate than those using zero-azimuth 3D SRME and (2) on-the-fly interpolation produces excellent results.
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