Robust filtering of ghost and peg-leg from sea floor seismic data

Raoni de Carvalho Costa Alves, Sérgio Adriano Moura Oliveira


ABSTRACT. The processing of compressional waves from the acoustic components of ocean bottom seismic data is performed decomposing the compressional wavefield on its upgoing an downgoing components, treating the first order multiples, composed by the overlap between the receiver ghost and peg-leg. The separation of these wavefields is achieved through the adaptive summation of the hydrophone and geophone components, usually on a least square sense. This method of separation is known as PZ summation, because it involves an operation between pressure and vertical particle velocity measurements. However, due to the difference in response of the pressure and velocity sensors, the premises assumed on the least square summation can be violated, degrading the results. To overcome these difficulties, a more robust method can be achieved using the L1 norm criterion for the adaptive sum. A comparison was made between the results obtained with the Iterative Reweighted Least Squares and Wiener-Levinson filters. The robustness of the L1 norm sum was demonstrated by applications on PZ summation of Ocean Bottom Cable data from the Jubarte area, in the Campos Basin, Brazil, showing improvements, especially when the multiples are present in the estimation window used to derive the filter coefficients.


PZ summation; Wiener-Levinson; IRLS; ghost; peg-leg

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