New methodology for Water Layer Multiple Attenuation in OBC Data

Soudani, Mohamed; Boelle, Jean Luc; Mars, Jérôme

doi:10.3997/2214-4609-pdb.1.p078

Cited by 1 publication

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“…Therefore, even after decomposition using , the multiples in P w up still cause problem. Soudani et al (2005), as an extention of Soubaras (1996), propose a combination of the two previously estimated acoustic wavefields in the τ– p domain to eliminate the first‐order of multiples: The effect of the subtraction is also illustrated in Fig. 6.…”

Section: Rf Decomposition Equationsmentioning

confidence: 99%

“…However, these methods suffer from the fact that there are many events at a constant offset that arrive at different angles, and hence the approximation breaks down. Recently, the acoustic decomposition procedure has been implemented in the τ -p domain (τ being the intercept time, p the horizontal slowness) and is therefore valid for all incidence angles (Lokshtanov 1993(Lokshtanov , 1995(Lokshtanov , 2000Soudani et al 2005Soudani et al , 2006.…”

Section: Introductionmentioning

confidence: 99%

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Receiver function decomposition of OBC data: theory

Edme

Singh²

2009

Geophysical Journal International

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S U M M A R YThis paper deals with theoretical aspects of wavefield decomposition of Ocean Bottom Cable (OBC) data in the τ -p domain, considering a horizontally layered medium. We present both the acoustic decomposition and elastic decomposition procedures in a simple and compatible way. Acoustic decomposition aims at estimating the primary upgoing P wavefield just above the ocean-bottom, whereas elastic decomposition aims at estimating the primary upgoing P and S wavefields just below the ocean-bottom. Specific issues due to the interference phenomena at the receiver level are considered. Our motivation is to introduce the two-step decomposition scheme called 'receiver function' (RF) decomposition that aims at determining the primary upgoing P and S wavefields (RF P and RF S , free of any water layer multiples). We show that elastic decomposition is a necessary step (acting as pre-conditioning) before applying the multiple removal step by predictive deconvolution. We show the applicability of our algorithm on a synthetic data example.

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