Statistical amplitude balancing/compensation techniques are widely used in the industry to prepare seismic data for amplitude variation with offset (AVO) processing and analysis. The intent of such statistical techniques is to compensate the data for the average signal decay with offset such that reflectors that are anomalous with respect to this average decay can be detected and analyzed. Statistical amplitude compensation techniques, however, suffer from a serious flaw when applied to data sets having low signal-to-noise ratios (S/N) that vary with offset. An artifact of this flaw is often a suppression of the AVO effects one is trying to detect. When S/N is low and decreases with offset, as is usually the case, the rms amplitude measurements that statistical techniques are based upon become increasingly dominated by noise as offset increases. This can lead to a suppression of the far offsets by the balancing scalars responding to a noise level that is increasing with offset.A noise-discriminating, statistical-amplitude compensation technique can be designed that counteracts the detrimental effects of noise. This technique is based on the premise that a common-midpoint (CMP) ensemble average of crosscorrelations of like offset data is proportional to the average signal amplitude corresponding to that offset. The average signal decay with offset can be estimated with this technique and used to amplitude compensate a data set for AVO analysis. The noise-discriminating statistical technique performs extremely well on synthetic data. When applied to a Gulf of Mexico data set having poor S/N characteristics, the technique also performs well and offers encouragement that it will be useful in actual practice.

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