Robust multiple suppression using adaptive beamforming

Minimum variance unbiased (MW) beamforming is a type of multichannel filtering which extracts coherent signals without distortion, whilst minimizing residual noise power. Adaptive beamforming estimates signal and noise characteristics as part of the extraction process. The adaptive beamformer used here is designed from models of primary and multiple reflection signals having parametrically specified moveout and amplitude variation with offset (MVO and AVO). Phase variation with offset (PVO) can also be included but it is not usually justified in practice. The resulting analysis provides data for input into AVO and PVO schemes for obtaining lithological information. Synthetic data examples illustrate details of implementation of parametric adaptive MVU beamforming and the response characteristics of the resultant design. Real data examples show that data-adaptive beamforming is more flexible and more effective in attenuating multiples in prestack common-midpoint seismic data than Radon transform methods. In common with other prestack multichannel processes, the advantages of beamforming are shown to best effect in data with a good signal-to-noise ratio.