Model-independent Travel-time Attributes for 2-D, Finite-offset Multicoverage Reflections

Zhang, Y.; Bergler, S.; Tygel, Martin; Hubral, Peter

doi:10.1007/s00024-002-8699-6

Cited by 9 publications

(3 citation statements)

References 10 publications

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“…The five‐parameter CO CRS stack formula is able to handle any type of reflection, P‐P and S‐S as well as P‐S or S‐P converted reflections. Here, we shall explain the theoretical background of the CO CRS stack (Zhang et al . 2001) and show results for synthetic primary P‐P reflection data.…”

Section: Introductionmentioning

confidence: 99%

Common‐reflection‐surface (CRS) stack for common offset

Zhang

Bergler

Hubral

2001

Geophysical Prospecting

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We provide a data‐driven macro‐model‐independent stacking technique that migrates 2D prestack multicoverage data into a common‐offset (CO) section. We call this new process the CO common‐reflection‐surface (CRS) stack. It can be viewed as the generalization of the zero‐offset (ZO) CRS stack, by which 2D multicoverage data are stacked into a well‐simulated ZO section. The CO CRS stack formula can be tailored to stack P‐P, S‐S reflections as well as P‐S or S‐P converted reflections. We point out some potential applications of the five kinematic data‐derived attributes obtained by the CO CRS stack for each stack value. These include (i) the determination of the geometrical spreading factor for reflections, which plays an important role in the construction of the true‐amplitude CO section, and (ii) the separation of the diffractions from reflection events. As a by‐product of formulating the CO CRS stack formula, we have also derived a formula to perform a data‐driven prestack time migration.

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Section: Introductionmentioning

confidence: 99%

Common‐reflection‐surface (CRS) stack for common offset

Zhang

Bergler

Hubral

2001

Geophysical Prospecting

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“…As a generalisation of the zero-offset CRS stack, the finite-offset CRS stack method has been used for simulating finite-offset seismic sections in the common-shot, commonmidpoint, and common-offset data configurations (Bergler et al 2001a;Bergler et al 2002b;Zhang et al 2001;Zhang, Bergler and Hubral 2002;Boelsen and Mann 2005a, b;Höcht Figure 2 Ray diagram for a paraxial raySRḠ in the vicinity of a central ray SRG in a 2D medium consisting of two isovelocity layers over a half-space with curved and smooth interfaces. (a) Wavefront curvature K 1 at receiver G for the wave originated S at source.…”

Section: Introductionmentioning

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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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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“…This representation is mandatory for applications which explicitely rely on propagation directions, e. g. redatuming and topography handling (see, e. g., Heilmann et al, 2006). In the general case of finite-offset simulation, two-way experiments are required to describe all stacking parameters (Zhang et al, 2002). For the simpler case of ZO simulation which will be considered in the following, two one-way experiments are sufficient: an exploding reflector experiment and an exploding point source experiment with the source located at the (unknown) NIP.…”

mentioning

confidence: 99%

Parameterization, stacking, and inversion of locally coherent events with the Common-Reflection-Surface Stack method

Mann¹

2009

71st EAGE Conference and Exhibition - Workshops and Fieldtrips

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Model-independent Travel-time Attributes for 2-D, Finite-offset Multicoverage Reflections

Cited by 9 publications

References 10 publications

Common‐reflection‐surface (CRS) stack for common offset

Common‐reflection‐surface (CRS) stack for common offset

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

Parameterization, stacking, and inversion of locally coherent events with the Common-Reflection-Surface Stack method

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