Receiver function decomposition of OBC data: theory

Edme, Pascal; Singh, Satish C.

doi:10.1111/j.1365-246x.2009.04147.x

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…Therefore, it is normal to witness direct events in the upgoing pressure and some level of subsurface-reflected events in downgoing stress or downgoing potentials. Detailed discussions on the seafloor interface effect and free surface-related effects can be found in the work of Edme and Singh [35]. An adaptive decomposition scheme usually includes four stages [13,34]:…”

Section: Methodsmentioning

confidence: 99%

Wavefield Decomposition of Ocean-Bottom Multicomponent Seismic Data with Composite Calibration Filters

Chu

2022

Remote Sensing

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Downgoing/upgoing P/S-wave decomposition of ocean-bottom seismic (OBS) multicomponent data can help suppress the water-layer multiples and cross-talks between P- and S-waves, and therefore plays an important role in seismic migration and construction of P- and S-wave velocity models. We proposed novel composite calibration filters by introducing an additional dimension to the calibration of the particle velocity components, extending the wave-equation-based adaptive decomposition method. We also modified the existing workflow by jointly using primary reflections at near-to-medium offsets and ocean-bottom refractions at far offsets in the calibration optimization. The decomposition scheme with the novel calibration filters yielded satisfactory results in a deep-water OBS field data decomposition example. Expected decomposition effects, such as the enhancement of primary reflections and the attenuation of water-layer multiple events, can be observed in the decomposed upgoing wavefields. An experiment illustrated the effectiveness of composite calibration filters that compensated for unexpected velocity errors along the offset dimension.

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Section: Methodsmentioning

confidence: 99%

Wavefield Decomposition of Ocean-Bottom Multicomponent Seismic Data with Composite Calibration Filters

Chu

2022

Remote Sensing

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“…is the least square predictive filter, once the downgoing is a delayed version of the upgoing wavefield (Soubaras, 1996;Ang et al, 2010;Edme and Singh, 2009;Hugonnet et al, 2011).…”

Section: The Pressure With Vertical Particle Velocity Summationmentioning

confidence: 99%

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

Alves

Oliveira

2023

Braz J Geophys

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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.

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“…Amundsen and Reitan (1995) and Soubaras (1996), published a method to process the acoustic components, the hydrophone and vertical geophone. Later Beresford and Janex (1996), Schalkwijk, Wapenaar and Verschuur (2003), Muijs, Robertsson and Holliger (2007), Wang and Grion (2008), Edme and Singh (2009), and Hugonnet et al (2011), approached the theme of the ocean bottom seismic data processing. Hugonnet et al (2011), summarized the aspects of the hydrophone and geophone processing, based on Soubaras (1996), and called it as PZ summation (pressure with vertical particle velocity), generalizing it to the three-dimensional notation, under the least square sense.…”

Section: Introductionmentioning

confidence: 99%

Robust unidimensional PZ Summation

2021

Proceeding of the 17th International Congress of the Brazilian Geophysical

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PZ summation is a mandatory step in ocean bottom sensor multi-component PP seismic processing flow, to attenuate receiver ghost and peg leg multiples from upgoing wavefield. It consists of an adaptive sum between the hydrophone and geophone components to adapt the vertical component to pressure in a least square sense. However, in spurious situations (nongaussian noised conditions), a robust method IRLS (Interactive Re-weighted Least Square) may be a better choice to run linear convolutional adapting filter. That proposal was applied on receiver gathers of the OBC data from Jubarte area, Brazil, using near receiver stack sections to quality control. Similar quality was obtained when short adaptive windows were used, but better quality was attained when applied on a selected near receiver stack section, as compared to Wiener-Levinson PZ summation results. The proposed method became a robust option to run PZ summation and perform the unidimensional upgoing and downgoing field decomposition, processing the first order receiver ghost and peg leg multiples.

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Receiver function decomposition of OBC data: theory

Cited by 6 publications

References 41 publications

Wavefield Decomposition of Ocean-Bottom Multicomponent Seismic Data with Composite Calibration Filters

Wavefield Decomposition of Ocean-Bottom Multicomponent Seismic Data with Composite Calibration Filters

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

Robust unidimensional PZ Summation

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