CRS‐stack‐based residual static correction

Koglin, Ingo; Mann, J.; Heilmann, Z.

doi:10.1111/j.1365-2478.2005.00562.x

Cited by 16 publications

(4 citation statements)

References 18 publications

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“…13a. The stacking parameter sections (picture omited) were used for CRS-based residual static correction (Koglin et al 2006) to build a time migration velocity model and to define optimum migration apertures (Spinner 2007). By this means, user interaction is reduced and imaging errors due to a wrong choice of processing parameters are prevented.…”

Section: Data Visualization and Preprocessingmentioning

confidence: 99%

GRIDA3—a shared resources manager for environmental data analysis and applications

et al. 2009

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GRIDA3 (Shared Resources Manager for Environmental Data Analysis and Applications) is a multidisciplinary project designed to deliver an integrated system to forge solutions to some environmental challenges such as the constant increase of polluted sites, the sustainability of natural resources usage and the forecast of extreme meteorological events. The GRIDA3 portal is mainly based on Web 2.0 technologies and EnginFrame framework. The portal, now at an advanced stage of development, provides end-users with intuitive Web-interfaces and tools that simplify job submission to the underneath computing resources. The framework manages the user authentication and authorization, then controls the action and job execution into the grid computing environment, collects the results and transforms them into an useful format on the client side. The GRIDA3 Portal framework will provide a problemsolving platform allowing, through appropriate access policies, the integration and the sharing of skills, resources and tools located at multiple sites across federated domains

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Section: Data Visualization and Preprocessingmentioning

confidence: 99%

GRIDA3—a shared resources manager for environmental data analysis and applications

et al. 2009

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“…This further illustrates the significant improvement in coherence. More details of this approach and its application to these data can be found in Koglin, Mann and Heilmann (2006).…”

Section: Real Data Examplementioning

confidence: 99%

CRS‐stack‐based seismic imaging considering top‐surface topography

Heilmann

Mann

Koglin

2006

Geophysical Prospecting

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In this case study we consider the seismic processing of a challenging land data set from the Arabian Peninsula. It suffers from rough top‐surface topography, a strongly varying weathering layer, and complex near‐surface geology. We aim at establishing a new seismic imaging workflow, well‐suited to these specific problems of land data processing. This workflow is based on the common‐reflection‐surface stack for topography, a generalized high‐density velocity analysis and stacking process. It is applied in a non‐interactive manner and provides an entire set of physically interpretable stacking parameters that include and complement the conventional stacking velocity. The implementation introduced combines two different approaches to topography handling to minimize the computational effort: after initial values of the stacking parameters are determined for a smoothly curved floating datum using conventional elevation statics, the final stack and also the related residual static correction are applied to the original prestack data, considering the true source and receiver elevations without the assumption of nearly vertical rays. Finally, we extrapolate all results to a chosen planar reference level using the stacking parameters. This redatuming procedure removes the influence of the rough measurement surface and provides standardized input for interpretation, tomographic velocity model determination, and post‐stack depth migration. The methodology of the residual static correction employed and the details of its application to this data example are discussed in a separate paper in this issue. In view of the complex near‐surface conditions, the imaging workflow that is conducted, i.e. stack – residual static correction – redatuming – tomographic inversion – prestack and post‐stack depth migration, leads to a significant improvement in resolution, signal‐to‐noise ratio and reflector continuity.

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“…However, the CRS stack method does not provide only ZO images, but also a set of wavefield attributes (also called CRS parameters: emergence angles and wave front curvatures) that have been exploited in several applications. These include, for example, velocity model building (Della‐Moretta et al 2001; Klüver 2006), multiple attenuation (Prüssmann et al 2006) or residual statics correction (Koglin et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Inverse Common-Reflection-Surface

Perroud¹,

Tygel

Freitas³

2010

Geophysical Journal International

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S U M M A R YThe Common-Reflection-Surface (CRS) stack method is a powerful tool to produce highquality stacked images of multicoverage seismic data. As a result of the CRS stack, not only a stacked section, but also a number of attributes defined at each point of that section, are produced. In this way, one can think of the CRS stack method as a transformation from data space to attribute space. Being a purely kinematic method, the CRS stack lacks amplitude information that can be useful for many purposes. Here we propose to fill this gap by means of a combined use of a zero-offset section (that could be a short-offset or amplitude-corrected stacked section) and common midpoint gather. We present an algorithm for an inverse CRS transformation, namely one that (approximately) transforms the CRS attributes back to data space. First synthetic tests provide satisfying results for the two simple cases of single dippingplane and single circular reflectors with a homogeneous overburden, and provide estimates of the range of applicability, in both midpoint and offset directions. We further present an application for interpolating missing traces in a near-surface, high-resolution seismic experiment, conducted in the alluvial plain of the river Gave de Pau, near Assat, southern France, showing its ability to build coherent signals, where recording was not available. A somewhat unexpected good feature of the algorithm, is that it seems capable to reconstruct signals even in muted parts of the section.The Common-Reflection-Surface (CRS) stack method is a recent data-driven time imaging process (see, e.g. Hubral 1999;Jäger et al. 2001 and also references therein) that has been originally proposed as an alternative to the classical normal moveout (NMO)-dip moveout (DMO) chain (Yilmaz 2000) to build seismic stacked, simulated zero-offset (ZO) time images of the subsurface. As already discussed elsewhere (Perroud & Tygel 2005), the CRS stack method has both advantages and disadvantages with respect to its alternative approaches. In fact, the adoption of the CRS stack method by the geophysical community has been until now only limited, because the classical NMO-DMO chain already provides good-quality robust results, so the need for a change is not obvious.However, the CRS stack method does not provide only ZO images, but also a set of wavefield attributes (also called CRS parameters: emergence angles and wave front curvatures) that have been exploited in several applications. These include, for example, velocity model building (Della-Moretta et al. 2001;Klüver 2006), multiple attenuation (Prüssmann et al. 2006) or residual statics correction (Koglin et al. 2006).The CRS stack method can be seen as a transformation from the data space (seismic amplitudes as a function of position and time) into attribute space (wavefield attributes as a function of position and time). Note that data space position variables include both midpoint and offset coordinates, while the attribute space position variables consist in the midpoint coordinat...

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CRS‐stack‐based residual static correction

Cited by 16 publications

References 18 publications

GRIDA3—a shared resources manager for environmental data analysis and applications

GRIDA3—a shared resources manager for environmental data analysis and applications

CRS‐stack‐based seismic imaging considering top‐surface topography

Inverse Common-Reflection-Surface

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