Adaptive multiple subtraction with wavelet-based complex unary Wiener filtersReport as inadecuate

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1 IFPEN - IFP Energies nouvelles 2 CGG - Compagnie Générale de Géophysique

Abstract : Adaptive subtraction is a key element in predictive multiple-suppression methods. It minimizes misalignments and amplitude differences between modeled and actual multiples, and thus reduces multiple contamination in the dataset after subtraction. The challenge consists in attenuating multiples without distorting primaries, despite the high cross-correlation between their waveform. For this purpose, this complicated wide-band problem is decomposed into a set of more tractable narrow-band problems using a 1D complex wavelet frame. This decomposition enables a single-pass adaptive subtraction via single-sample unary complex Wiener filters, consistently estimated on overlapping windows in a complex wavelet transformed domain. Each unary filter compensates amplitude differences within its frequency support, and rectifies more robustly small and large misalignment errors through phase and integer delay corrections. This approach greatly simplifies the matching filter estimation and, despite its simplicity, compares promisingly with standard adaptive 2D methods, on both synthetic and field data.

Keywords : complex wavelet unary wiener filter seismic signal processing echo multiples processing filtering crosscorrelation wavelet hilbert transform Morlet wavelet adaptive filters multiple removal

Author: Sergi Ventosa - Sylvain Le Roy - Irène Huard - Antonio Pica - Hérald Rabeson - Patrice Ricarte - Laurent Duval -



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