CRP‐scans: 3D PreSDM velocity analysis via zero‐offset tomographic inversion

Audebert, F.; Diet, J.P.; Guillaume, P.; Jones, Ian F.; Zhang, X.

doi:10.1190/1.1885786

Cited by 21 publications

(5 citation statements)

References 3 publications

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“…We showed that in a particular class of velocity models, admitting dependence in depth only, the retracing of DSR rays is unnecessary. In yet a different approach to velocity continuation, namely through so‐called common reflection‐point (CRP) stacks (Audebert et al 1997) based on a class of velocity model perturbations of the type, c (α; z , x ) =α c 0 ( z , x ), the ray geometry can be kept the same. However, unlike in our approach, the Kirchhoff summation has to be repeated for every α.…”

Section: Discussionmentioning

confidence: 99%

Velocity continuation in the downward continuation approach to seismic imaging

Duchkov¹,

Hoop²

2009

Geophysical Journal International

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S U M M A R YOne way of developing a wave-equation approach to seismic imaging is based on the concept of downward continuation of observed surface reflection data. Seismic imaging is typically based on the single scattering approximation, and assumes the knowledge of a smooth background model in which the mentioned downward continuation is carried out using the double-squareroot (DSR) equation. The downward continued data are subjected, at each depth, to an imaging condition generating an image or to an angle transform generating common-image gathers. Motivated by the problem of estimation of the background model we develop a framework for velocity continuation in the downward continuation approach to seismic imaging. Velocity continuation is a term used to describe the transformation of a common-image gather (or image) obtained in an 'initial' background velocity model into a gather (image) obtained in another 'final' model. We construct evolution equations for velocity continuation. We then introduce the concept of data-extended-image 'volume'. In the data-extended-image 'volume' we consider so-called extended images, which are functions of subsurface source-receiver midpoint, subsurface source-receiver offset and depth. We introduce and analyse velocity continuation of the individual components that make up the process of migration, imaging and the transformation to angle common-image gathers.

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Section: Discussionmentioning

confidence: 99%

Velocity continuation in the downward continuation approach to seismic imaging

Duchkov¹,

Hoop²

2009

Geophysical Journal International

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“…This holds true especially for the AVO distribution, angle-dependent reflection coefficients, fluid budget, and its migration paths, which are related to the earthquake phenomena along the megathrust (e.g. Bangs et al, 2015;Sallarès and Ranero, 2019) and may be investigated in future studies.…”

Section: Lower-slope Nrm Tomographymentioning

confidence: 98%

“…The velocity field may be determined during PSDM by performing velocity analysis on selected locations using depth-focusing error analysis (Audebert and Diet, 2008;MacKay and Abma, 1992) or hyperbolic residual move-out (RMO) correction of common image point (CIP) gathers followed by a simple vertical Dix inversion (Dix, 1955) at each location independently. Thus a structural velocity model (Audebert et al, 1997) may be built manually by defining depth horizons. Manual picking or pre-definition of depth horizons, however, are not only time-consuming for seismic processors but, more importantly, may also lead to a subjective interpretation bias (Jones, 2003).…”

Section: Introductionmentioning

confidence: 99%

Reflection tomography by depth warping: a case study across the Java trench

et al. 2022

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Abstract. Accurate subsurface velocity models are crucial for geological interpretations based on seismic depth images. Seismic reflection tomography is an effective iterative method to update and refine a preliminary velocity model for depth imaging. Based on residual move-out analysis of reflectors in common image point gathers, an update of the velocity is estimated by a ray-based tomography. To stabilize the tomography, several preconditioning strategies exist. Most critical is the estimation of the depth error to account for the residual move-out of the reflector in the common image point gathers. Because the depth errors for many closely spaced image gathers must be picked, manual picking is extremely time-consuming, human biased, and not reproducible. Data-driven picking algorithms based on coherence or semblance analysis are widely used for hyperbolic or linear events. However, for complex-shaped depth events, purely data-driven picking is difficult. To overcome this, the warping method named non-rigid matching is used to estimate a depth error displacement field. Warping is used, for example, to merge photographic images or to match two seismic images from time-lapse data. By matching a common image point gather against its duplicate that has been shifted by one offset position, a locally smooth-shaped displacement field is calculated for each data sample by gather matching. Depending on the complexity of the subsurface, sample tracking through the displacement field along predefined horizons or on a simple regular grid yields discrete depth error values for the tomography. The application to a multi-channel seismic line across the Sunda subduction zone offshore Lombok island, Indonesia, illustrates the approach and documents the advantages of the method to estimate a detailed velocity structure in a complex tectonic regime. By incorporating the warping scheme into the reflection tomography, we demonstrate an increase in the velocity resolution and precision by improving the data-driven accuracy of depth error picks with arbitrary shapes. This approach offers the possibility to use the full capacities of tomography and further leads to more accurate interpretations of complex geological structures.

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“…Among the decades various techniques were proposed to obtain more precise velocity models e.g. reflection tomography (Stork, 1992), CRP-scan (Audebert et al, 1997) or stereotomography (Lambaré, 25 2008) (see e.g. Jones et al, 2008, for detailed review).…”

Section: Introductionmentioning

confidence: 99%

Crustal-scale depth imaging via joint FWI of OBS data and PSDM of MCS data: a case study from the eastern Nankai Trough

Górszczyk

Operto

Schenini

et al. 2019

Preprint

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Abstract. Imaging via Pre-Stack Depth Migration (PSDM) from reflection towed-streamer Multi-Channel Seismic (MCS) data at the scale of the whole crust is inherently difficult. This mainly results because the depth-penetration of the seismic wavefield is controlled, firstly (i) by the acquisition design, like streamer length and air-gun source configuration, and secondly (ii) by the complexity of the crustal structure. Indeed, the limited length of the streamer makes the estimation of velocities from deep targets challenging due to the velocity-depth ambiguity. The problem is even more pronounced when processing 2D seismic data, due to the lack of multi-azimuthal coverage. Therefore, in order to broaden our knowledge about the deep crust using seismic methods, one shall target the development of specific imaging workflows integrating different seismic data. Here we propose the combination of velocity model-building using (i) first-arrival traveltime tomography (FAT) and full-waveform inversion (FWI) of wide-angle/long-offset data collected by stationary Ocean Bottom Seismometers (OBS) and (ii) PSDM of short-spread towed-streamer MCS data for reflectivity imaging, using the former velocity model as background model. We present an application of such workflow to seismic data collected by Japan Agency for Marine-Earth Science and Technology (JAMSTEC) and Institut Français de Recherche pour l’Exploitation de la Mer (IFREMER) in the eastern Nankai Trough (Tokai area) during the 2000/2001 SFJ experiment. We show that the FWI model, although derived from OBS data, provides yet an acceptable background velocity field for the PSDM of the MCS data. Furthermore, from the initial PSDM, we first refine the FWI background velocity model by minimizing the residual moveouts (RMO) picked in the prestack migrated volume through slope tomography (ST), from which we generate a better focused migrated image. Such integration of different seismic data sets and leading-edge imaging techniques led to optimal imaging results at different resolution levels. That is, the large-to-intermediate scale crustal units identified in the high-resolution FWI velocity model extensively complement the short-scale reflectivity inferred from the MCS data to better constrain the structural factors controlling the geodynamics of the Nankai Trough area.

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CRP‐scans: 3D PreSDM velocity analysis via zero‐offset tomographic inversion

Cited by 21 publications

References 3 publications

Velocity continuation in the downward continuation approach to seismic imaging

Velocity continuation in the downward continuation approach to seismic imaging

Reflection tomography by depth warping: a case study across the Java trench

Crustal-scale depth imaging via joint FWI of OBS data and PSDM of MCS data: a case study from the eastern Nankai Trough

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