F-XY Wavefield de-aliasing for acquisition configurations leading to coarse sampling

Huard, Irène; Medina, S.; Spitz, Simon

doi:10.3997/2214-4609.201408691

Cited by 8 publications

(6 citation statements)

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“…Various interpolation techniques have been devised to cure this problem (Jakubowicz, 1994;Huard et al, 1996). Impressive examples are shown in Wombell and Williams (1995) and Manin and Spitz (1995).…”

Section: Source Interval Number Of Sources and Foldmentioning

confidence: 98%

3D Seismic Survey Design, Second edition

Vermeer¹

2012

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Section: Source Interval Number Of Sources and Foldmentioning

confidence: 98%

3D Seismic Survey Design, Second edition

Vermeer¹

2012

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“…The finitistic dimension of a finite dimensional algebra is the supremum of the projective dimensions of finitely generated modules having finite projective dimension and the Finitistic Dimension Conjecture asserts that this supremum is finite. The conjecture has been proven directly in a number of cases, for example, [13,16,19,4], and attempts have been made to study the conjecture by reducing the conjecture to 'simpler' rings [17,7,8,9,6], but the conjecture remains open. A survey on the state of the art until 2007 can be found in [9].…”

Section: Introductionmentioning

confidence: 99%

Convex subquivers and the finitistic dimension

Green¹,

Marcos²

2017

Illinois J. Math.

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Let Q be a quiver and K a field. We study the interrelationship of homological properties of algebras associated to convex subquivers of Q and quotients of the path algebra KQ. We introduce the homological heart of Q which is a particularly nice convex subquiver of Q. For any algebra of the form KQ/I, the algebra associated to KQ/I and the homological heart have similar homological properties. We give an application showing that the finitistic dimension conjecture need only be proved for algebras with path connected quivers.

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“…Porsani (1999) proposes a half-step PEF scheme that makes the interpolation process more efficient. Huard et al (1996) and Wang (2002) extend f -x trace interpolation to higher spatial dimensions. Gulunay (2003) introduces an algorithm similar to f -x prediction filtering, which has an elegant representation in the f -k domain.…”

Section: Introductionmentioning

confidence: 99%

Seismic data interpolation beyond aliasing using regularized nonstationary autoregression

Fomel

2011

GEOPHYSICS

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Seismic data are often inadequately or irregularly sampled along spatial axes. Irregular sampling can produce artifacts in seismic imaging results. We have developed a new approach to interpolate aliased seismic data based on adaptive prediction-error filtering (PEF) and regularized nonstationary autoregression. Instead of cutting data into overlapping windows (patching), a popular method for handling nonstationarity, we obtain smoothly nonstationary PEF coefficients by solving a global regularized least-squares problem. We employ shaping regularization to control the smoothness of adaptive PEFs. Finding the interpolated traces can be treated as another linear least-squares problem, which solves for data values rather than filter coefficients. Compared with existing methods, the advantages of the proposed method include an intuitive selection of regularization parameters and fast iteration convergence. The technique was tested on benchmark synthetic and field data to prove it can successfully reconstruct data with decimated or missing traces.

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F-XY Wavefield de-aliasing for acquisition configurations leading to coarse sampling

Cited by 8 publications

References 0 publications

3D Seismic Survey Design, Second edition

3D Seismic Survey Design, Second edition

Convex subquivers and the finitistic dimension

Seismic data interpolation beyond aliasing using regularized nonstationary autoregression

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