Numerical analysis of the finite-offset common-reflection-surface traveltime approximations

Garabito, German; Oliva, Pedro Andrés Chira; Cruz, João Carlos Ribeiro

doi:10.1016/j.jappgeo.2011.03.005

Cited by 20 publications

(8 citation statements)

References 16 publications

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“…Considering a given known velocity model, the curvatures K 1 , K 2 , and K 3 , as well as the angles

β_{S}

and

β_{G}

associated to a central ray, can be calculated by forward modelling using a ray‐tracing algorithm (Červený and Pšenčík ; Hubral and Krey ). For a more detailed geometrical description of the CRS parameters, we refer the readers to the work of Garabito, Chira and Cruz .…”

Section: Theoretical Aspectsmentioning

confidence: 99%

“…This behaviour of the relative errors of the CRS parameters was also observed in the sensitivity analysis by Garabito et al . , where it has been shown that curvature K 1 has the lowest sensitivity, curvatures K 2 and K 3 have intermediate sensitivity, and angles β S and β G have the highest sensitivity. In the convergence tests, we observed that parameter K 1 has a large fluctuation interval, which is caused by very small changes in the value of the cost function.…”

Section: Application To Synthetic Datasetmentioning

confidence: 99%

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Finite‐offset common reflection surface stack using global optimisation for parameter estimation: a land data example

Garabito

Cruz

Söllner

2016

Geophysical Prospecting

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In recent years, several research works dealing with velocity model independent seismic imaging have been published. These methods are capable of simulating arbitrary offset seismic sections by stacking a set of measured prestack seismic data along paraxial travel‐time surfaces. Hyperbolic common‐reflection‐surface travel‐time approximation is one of the most robust descriptions, which simulates not only zero‐offset but also finite‐offset sections with high accuracy from noisy multi‐coverage seismic data. In order to reconstruct seismic reflection events in common‐offset sections, the common‐reflection‐surface travel‐time approximation depends on five kinematic attributes (or parameters) for each selected point of the common‐offset seismic section. The main challenge of this method is to provide a computationally efficient data‐driven strategy for accurately determining the best set of parameters. Here, we introduce an approach for simultaneously estimating the five parameters from prestack seismic data by a very fast simulated annealing optimisation algorithm. For each sample point of the common‐offset section to be simulated, we determine only one set of common reflection surface attributes corresponding to the global maximum or the event with highest coherency. We applied our method of simultaneous global optimisation on synthetic and real data examples and showed the potential of the proposed strategy to enhance the reflection events in noisy data, even with very low signal‐to‐noise ratio. Finally, we demonstrate the regularisation capability of our method in a land seismic data example with missing traces for near, middle, and far offsets. In order to better appreciate the field data results, we present the time‐migrated sections with and without application of the proposed regularisation strategy.

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“…Considering a given known velocity model, the curvatures K 1 , K 2 , and K 3 , as well as the angles

β_{S}

and

β_{G}

Section: Theoretical Aspectsmentioning

confidence: 99%

Section: Application To Synthetic Datasetmentioning

confidence: 99%

Finite‐offset common reflection surface stack using global optimisation for parameter estimation: a land data example

Garabito

Cruz

Söllner

2016

Geophysical Prospecting

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“…This implies that

K_{CMP}^{G} = K_{C S}^{G}

, which, substituted into equation , results in a so‐called diffraction traveltime approximation of the form (Zhang et al . ; Garabito, Oliva and Cruz )

t_{C O}^{2} (Δ m, h_{0}) = {(() t_{C O}^{0} + A_{C O} Δ m)}^{2} + B_{C O} Δ m^{2},

with the definition of the parameters

A_{C O}

and

B_{C O}

given by

\begin{matrix} true \\ right A_{C O} & = & left \frac{prefixsin β_{G}}{V_{G}} - \frac{prefixsin β_{S}}{V_{S}} \\ right B_{C O} & = & left t_{C O}^{0} (() \frac{{prefixcos}^{2} β_{G}}{V_{G}} K_{CMP}^{G} - \frac{{prefixcos}^{2} β_{S}}{V_{S}} K_{CMP}^{S}) . \end{matrix}

Here,

K_{italicCMP}^{S}

and

K_{italicCMP}^{G}

represent, respectively, the wavefront curvatures measured at the source and receiver of the central ray due to a point source at the diffraction point.…”

Section: Diffraction Traveltime Approximationsmentioning

confidence: 99%

“…1). This implies that K G CMP = K G CS , which, substituted into equation (1), results in a so-called diffraction traveltime approximation of the form (Zhang et al 2001;Garabito, Oliva and Cruz 2011)…”

Section: Modified Common-reflection-surface Diffraction Traveltime Apmentioning

confidence: 99%

2D common‐offset traveltime based diffraction enhancement and imaging

Asgedom

Gelius

Tygel

2013

Geophysical Prospecting

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The diffracted energy in a seismic recording contains key information about small‐scale inhomogeneities or discontinuities of the subsurface. Diffractions can therefore lead to high‐resolution imaging of subsurface structures associated with hydrocarbon traps. However, seismic diffracted signals are often much weaker than specular reflections and consequently require enhancement before they can be utilized. In this paper diffractions are enhanced relative to reflections based on two traveltime techniques, namely the modified common‐reflection‐surface approach, which uses the common‐reflection‐surface technique with some modification to tailor it for diffractions and the replacement‐media approach derived here for the purpose of a simplified parametrization of diffraction traveltimes. Both approaches are implemented in the common‐offset domain with the use of a finite‐offset central ray unlike the zero‐ or small‐offset diffraction enhancement techniques that use a zero‐offset central ray. The validity of the two moveout expressions is tested using velocity data taken from a smooth isotropic Marmousi model. A feasibility test was also carried out with respect to the new replacement‐media traveltime approximation addressing the various effects of signal‐to‐noise ratio, depth and lateral displacement of the diffractor location. Finally, diffraction enhancement and imaging was performed on 2D seismic data from the Jequitinhonha basin offshore Brazil. Diffractions were significantly enhanced and a high‐resolution image of the discontinuities of the subsurface was obtained.

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“…However, it was based on a predefined velocity model that makes it a velocity model-dependent approach. Garabito et al (2011) introduced the surface operator of the CDS method that was a special case of the data-driven CDS, when only the dominant surface is considered as the final stacking operator. Yang et al (2012) called the situation caused by conflicting dip problems as dip discrimination phenomenon and handled this problem with migration/de-migration approach.…”

Section: Introductionmentioning

confidence: 99%

Seismic image enhancement in post stack depth migration by finite offset CDS stack method

Soleimani

Balarostaghi

2016

J Petrol Explor Prod Technol

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Seismic imaging in complex geological structures such as thrust belts and areas with complex geological structures is affected by several factors that often lead to poor-quality final result. Usually such structures produce locally very steep dips, strong lateral variations in velocity and abrupt truncation of the reflectors. Common reflection surface stack is a macro velocity model independent method that is introduced for seismic imaging in complex media. However, this method has some drawbacks in imaging of low-quality data from complex structures. Many improvements to this method have been introduced in several researches to overcome this drawback. However, the problem of conflicting dips situation is still a problematic issue in this method. In this study, a new method, called finite offset common diffraction surface (FO-CDS) stack, is introduced to overcome this problem and remove some geological interpretation ambiguities in seismic sections. This method is based on improving the CDS stack operator, with the idea of partial common reflection surface stack. This modification will enhance the quality of the final seismic image, where it suffers from conflicting dips problem and low signal to noise ratio. The new idea is to change the operator of the CDS equation into a finite offset mode in different steps for each sample. Subsequently, a time-variant linear function is designed for each sample to define the offset range using FO-CDS operator. The width of this function is designed according to the Fresnel zone. The new operator was applied on a synthetic and a real low fold land data. Results show the ability of the new method in enhancing the quality of the stacked section in the presence of faults and conflicting dips.

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Numerical analysis of the finite-offset common-reflection-surface traveltime approximations

Cited by 20 publications

References 16 publications

Finite‐offset common reflection surface stack using global optimisation for parameter estimation: a land data example

Finite‐offset common reflection surface stack using global optimisation for parameter estimation: a land data example

2D common‐offset traveltime based diffraction enhancement and imaging

Seismic image enhancement in post stack depth migration by finite offset CDS stack method

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