Deterministic Estimation of a Wavelet Using Impedance Type Technique*

Loewenthal, Dan; Lee, S. S.; Gardner, G. H. F.

doi:10.1111/j.1365-2478.1985.tb00791.x

Cited by 28 publications

(13 citation statements)

References 5 publications

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“…Contrary to vertical arrays, beamforming with horizontal arrays of hydrophones cannot separate up-and downgoing waves. However, combining collocated hydrophones and vertical particle velocity sensors, e.g., with an ocean bottom sensor (OBS) network, enable an up/down separation scheme 22,23 which, combined with beamforming, provide an acoustic or seismic data measurement system able to point in any direction (not necessarily vertical) or filter unwanted direction of arrival, with a resolution related to the beampattern. By implementing a spatial filter that attenuates horizontally propagating noise, the subsurface reflection peaks in the EGF are enhanced.…”

Section: Introductionmentioning

confidence: 99%

Spatial filtering in ambient noise interferometry

Carrière

Gerstoft

Hodgkiss

2014

The Journal of the Acoustical Society of America

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Theoretically, the empirical Green's function between a pair of receivers can be extracted from the cross correlation of the received diffuse noise. The diffuse noise condition rarely is met in the ocean and directional sources may bias the Green's function. Here matrix-based spatial filters are used for removing unwanted contributions in the cross correlations. Two methods are used for solving the matrix filter design problem. First a matrix least-square problem is solved with a low-rank approximation of the pseudo-inverse, here, derived for linear and planar arrays. Second, a convex optimization approach is used to solve the design problem reformulated with ad hoc constraints. The spatial filter is applied to real-data cross correlations of elements from a linear array to attenuate the contribution of a discrete interferer. In the case of a planar array and simulated data, a spatial filter enables a passive upgoing/downgoing wavefield separation along with an efficient rejection of horizontally propagating noise. The impact of array size and frequency band on the filtered cross correlations is discussed.

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Section: Introductionmentioning

confidence: 99%

Spatial filtering in ambient noise interferometry

Carrière

Gerstoft

Hodgkiss

2014

The Journal of the Acoustical Society of America

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“…This technique is called dual-sensor summation. Loewenthal (1985) first proposed the possibility of wavelength separation based on data recorded by hydrophones and geophones. Fred et al (1989) first suggested a robust dual-sensor summation method, which they called "reflection coefficient method" to eliminate multiples, and confi rmed the matching factor (1 + K r )/(1 -K r ), K r as the bottom reflection coefficient.…”

Section: Introductionmentioning

confidence: 99%

Ocean-bottom cable data multiple suppression based on equipoise pseudo-multichannel matching filter

et al. 2015

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The effect of strong refl ection interfaces, such as free surface, seabed, is strong; thus, the coupling of multiples and waves reduces the quality of ocean-bottom cable seismic data. Using the different polarity response of hydrophones and geophones to downgoing wave fi elds, dual-sensor summation can eliminate receiver-side multiples, enhance primaries, and improve the resolution of seismic data. We present a dual-sensor summation method based on the equipoise pseudo-multichannel adaptive matching filter. Compared with traditional methods, the proposed method is totally data driven and does not depend on the refl ection coeffi cient; moreover, good results are obtained using synthetic and real data.

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“…This kind of decomposition involves the combination of the vertical geophone and hydrophone component data and is often referred to as the PZ summation or as the dual-sensor method. Some of the early methods for combining these components to attenuate energy arriving from above through the water column were developed by Haggerty (1956), White (1965), Gal'perin (1974) and Loewenthal et al (1985). The dual sensor method was presented by Barr & Sanders (1989), and further extended by Dragoset & Barr (1994), Paffenholz & Barr (1995), Barr (1997) and Barr et al (1997).…”

Section: Introductionmentioning

confidence: 99%

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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Deterministic Estimation of a Wavelet Using Impedance Type Technique*

Cited by 28 publications

References 5 publications

Spatial filtering in ambient noise interferometry

Spatial filtering in ambient noise interferometry

Ocean-bottom cable data multiple suppression based on equipoise pseudo-multichannel matching filter

Receiver function decomposition of OBC data: theory

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