Acquisition using simultaneous sources

Hampson, Gary; Stefani, Joe; Herkenhoff, Fred

doi:10.1190/1.3063930

Cited by 35 publications

(13 citation statements)

References 9 publications

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“…In general, the weaker the amplitudes in the diagonal of the Hessian, more points surrounding the diagonal are required to make an accurate approximation in the truncated Hessian. However, the truncated Hessian approximation breaks down for the case of blended-source acquisition geometry (Beasley et al, 1998;Beasley, 2008;Berkhout, 2008;Hampson et al, 2008), where energy of the Hessian is no longer concentrated around the diagonal points, but spreads everywhere in the model domain ). In these situations, a data-domain implementation of the linearized inversion is preferred ).…”

Section: Chapter 2 Target-oriented Wavefield Least-squares Migrationmentioning

confidence: 99%

Imaging and velocity analysis by target-oriented wavefield inversion

Tang

2011

GEOPHYSICS

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This thesis develops a novel target-oriented inversion framework that uses wavefields as carriers of information to image both low-and high-wavenumber components of the earth model in complex geological settings, such as subsalt regions. The lowwavenumber component of the earth model is often known as background velocity, whereas the high-wavenumber component of the earth model is often known as reflectivity. I address the problem of reflectivity imaging with target-oriented wavefield least-squares migration, and the problem of velocity estimation with target-oriented wavefield tomography.Reflectivity images of the subsurface are commonly produced by prestack depth migration. When the overburden is complex and the reflectors are unevenly or insufficiently illuminated, the migration operator alone is inadequate to provide an optimal image.I tackle the problem of distorted illumination in reflectivity imaging by wavefield least-squares migration. I formulate least-squares migration in the image domain and solve it in a target-oriented fashion. In the image-domain formulation, explicit computation of the Hessian operator (the resolution function that measures the illumination deficiency of the imaging system) is the most important and challenging step. I develop a novel method based on phase encoding to efficiently and accurately compute the target-oriented Hessian operator. By design, the phase-encoded Hessian converges to the exact Hessian either deterministically (for plane-wave phase encoding) or statistically (for random phase encoding), while having the important advantages that no Green's functions need to be stored during computation and that v the number of wavefield propagations is also drastically reduced. The target-oriented Hessian operator is then used to recover the reflectivity by iterative inverse filtering. I regularize the inversion with dip constraints, which naturally incorporate interpreted geological information into the inversion.Accurate imaging of the reflectivity also requires an accurate background velocity model. High-quality velocity model-building in complex geology requires wavefieldbased velocity analysis to properly model band-limited wave phenomena. However, the high cost and lack of flexibility of target-oriented model-building prevent this method from being widely used in practice.I overcome the cost and flexibility issues of wavefield-based migration velocity analysis by developing target-oriented wavefield tomography. Target-oriented wavefield tomography is achieved by synthesizing a new data set specifically for velocity analysis. The new data set is generated based on an initial unfocused target image and by a novel application of generalized Born wavefield modeling, which correctly preserves velocity kinematics by modeling both zero and non-zero subsurface-offset-

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Section: Chapter 2 Target-oriented Wavefield Least-squares Migrationmentioning

confidence: 99%

Imaging and velocity analysis by target-oriented wavefield inversion

Tang

2011

GEOPHYSICS

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“…The benefits of using two or more shots overlapping during seismic surveys have been demonstrated (see, for example, Beasley et al, 1998;Beasley, 2008;Berkhout, 2008;Hampson et al, 2008;Berkhout and Blacquière, 2013). In simultaneous seismic data acquisition each subsurface grid point can be illuminated from a larger number of angles and, more importantly, from larger angles.…”

Section: Introductionmentioning

confidence: 99%

Benefits of Blended Acquisition with Dispersed Source Arrays (DSA)

Caporal¹,

Blacquière²

2015

Proceedings

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SUMMARYIn blended data acquisition, the recorded wave field is incoherent. Nevertheless blended source units in the arrays are historically chosen to be equal. We propose to abandon this constraint. This allows us to suggest the exploitation of inhomogeneous blended sources, together representing a Dispersed Source Array (DSA). Each source unit involved in the survey might be dedicated to a narrow and arbitrary frequency bandwidth, without the need to satisfy the wideband requirement. Inhomogeneous blending with DSA has several attractive potential advantages. The design of such sources is potentially simpler. In addition, the DSA concept opens the possibility to use tow depths and spatial sampling intervals that are optimum for specific sources. The whole inhomogeneous ensemble of sources incorporated to the array is designed to cover the entire seismic bandwidth of interest.

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“…The term source blending was introduced by Berkhout (2008), and the differences with plane wave synthesis (Rietveld and Berkhout, 1994) were pointed out. A near-simultaneous shooting technique with small random time delays between impulsive sources was presented by Hampson et al (2008). Vibroseis acquisition by means of Simultaneous Pseudorandom Sweep Technology (SPST) has been suggested by Sallas et al (2008).…”

Section: Introductionmentioning

confidence: 99%

Separation of blended data by iterative estimation and subtraction of blending interference noise

2011

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Seismic acquisition is a trade-off between economy and quality. In conventional acquisition the time intervals between successive records are large enough to avoid interference in time.To obtain an efficient survey, the spatial source sampling is therefore often (too) large. However, in blending, or simultaneous acquisition, temporal overlap between shot records is allowed. This additional degree of freedom in survey design significantly improves the quality or the economics or both. Deblending is the procedure of recovering the data as if they were acquired in the conventional, unblended way. A simple least-squares procedure, however, does not remove the interference due to other sources, or blending noise. Fortunately, the character of this noise is different in different domains, e.g., it is coherent in the common source domain, but incoherent in the common receiver domain. This property is used to obtain a considerable improvement. We propose to estimate the blending noise and subtract it from the blended data. The estimate does not need to be perfect because our procedure is iterative. Starting with the least-squares deblended data, the estimate of the blending noise is obtained via the following steps: sort the data to a domain where the blending noise is incoherent; apply a noise suppression filter; apply a threshold to remove the remaining noise, ending up with (part of) the signal; compute an estimate of the blending noise from this signal. At each iteration, the threshold can be lowered and more of the signal is recovered. Promising results were obtained with a simple implementation of this method for both impulsive and vibratory sources. Undoubtedly, in the future algorithms will be developed for the direct processing of blended data. However, currently a highquality deblending procedure is an important step allowing the application of contemporary processing flows.

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Acquisition using simultaneous sources

Cited by 35 publications

References 9 publications

Imaging and velocity analysis by target-oriented wavefield inversion

Imaging and velocity analysis by target-oriented wavefield inversion

Benefits of Blended Acquisition with Dispersed Source Arrays (DSA)

Separation of blended data by iterative estimation and subtraction of blending interference noise

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