Non‐linear tomography for time imaging

Lambaré, Gilles; Deladerrière, N.; Traonmilin, Yann; Touré, J. P.; Le-Moigne, José; Herrmann, P.

doi:10.1190/1.3255701

Cited by 2 publications

(5 citation statements)

References 7 publications

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“…Our non-linear slope tomography handles structural dips and elevation, as well as multi-azimuth and WAZ acquisition geometries, and is applicable in both time and depth domains. Effective parameters V eff and η eff will be updated in the timemigrated domain (Lambaré et al, 2009) and the interval parameter V int will be updated in the depth-migrated domain from the same set of kinematic invariants (Figure 1). An example of the depth tomography result is given in Figure 3; the RMO histogram before inversion denotes too low velocities with a large amount of negative RMO values (predicted RMO at maximum offset).…”

Section: Time and Depth Tomography And Imaging Resultsmentioning

confidence: 99%

“…The mandatory structural information is computed using a robust 3D automatic picking of coherent events in the COVs after structurally consistent filtering (Traonmilin et al, 2008;Siliqi et al, 2009). WAZ non-linear slope tomography can then invert azimuthally varying WAZ RMO for updating either interval depth V int and ε fields (Guillaume et al, 2008) or effective time V eff and η eff fields (Lambaré et al, 2009). In this paper, we present the application to a land dataset from a high density WAZ surface survey, and migration results for both time and depth velocity model building routes with emphasis on azimuth related focusing issues.…”

Section: Introductionmentioning

confidence: 99%

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Can we correct for azimuthal variations of residual move‐out in land WAZ context, using depth non‐linear slope tomography? An imaging case history

Montel

Zimine

Lambaré

et al. 2010

SEG Technical Program Expanded Abstracts 2010

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High-density wide azimuth (WAZ) land surface acquisitions have demonstrated superior imaging capabilities. Apart from the traditional poor signal-to-noise ratio of land data we face a new challenge: the necessity of reconciling the kinematics of the various azimuths. In this paper, we present an imaging case history involving WAZ non-linear slope tomography. Using surface information (kinematic invariants), velocity model updates are performed both in depth and time. We chose to start from an initial pre-stack time migrated (PreSTM) dataset. After applying a structurally consistent filtering to improve the S/N ratio on stacked data, we used a dense automated tool for dip picking. In parallel residual move-out (RMO) was computed on all azimuths simultaneously. Our case study demonstrates that WAZ non-linear slope tomography in the depth domain greatly improves the imaging of the structures when compared to the initial PreSTM result. We observe that even if tomography in the time domain significantly enhances imaging, it cannot successfully honour the kinematics of the various azimuths within the constraints of time imaging assumptions. On the contrary, WAZ non-linear slope tomography in the depth domain offers an efficient way to reconcile these kinematics, thus promoting the use of depth imaging when processing high-density WAZ data, even in the context of mild geological complexity.

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Section: Time and Depth Tomography And Imaging Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Can we correct for azimuthal variations of residual move‐out in land WAZ context, using depth non‐linear slope tomography? An imaging case history

Montel

Zimine

Lambaré

et al. 2010

SEG Technical Program Expanded Abstracts 2010

Self Cite

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“…Here we use here the extension of the method to WAZ data (Montel et al, 2010) both for depth and time velocity update (Lambaré et al, 2009). It is coupled with a dense WAZ RMO picking (Lecerf et al, 2009).…”

Section: Non-linear Slope Tomography For Time and Depth Imagingmentioning

confidence: 99%

“…Here we use the RMO information picked on PreSTM gathers and dips, and skeletons picked on a near-offset stack using an automatic structural mapping of coherent horizons (Siliqi et al, 2009). Effective parameters V eff and η eff will be updated in the time-migrated domain (Lambaré et al, 2009) whereas interval parameter V int will be updated in the depth-migrated domain from the same set of kinematic invariants (Figure 3).…”

Section: Application To the Land Waz Datasetmentioning

confidence: 99%

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Azimuthal anisotropy? The time and depth imaging points of view: an imaging case history

Lambaré¹,

Zimine²,

Guillaume³

et al. 2010

72nd EAGE Conference and Exhibition - Workshops and Fieldtrips

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The superior imaging capabilities of wide azimuth (WAZ) are now well established. Processing such acquisitions requires adaptation of the processing workflows and tools. For example the recorded azimuthal information must be kept and the tools should be able to deal with the increased amount of data. Concerning the velocity model building, a question remains on the need of introducing azimuthal anisotropy. In this paper we address this point with a time and depth imaging case study for a high density wide azimuth (WAZ) land surface acquisition. We show on this dataset that azimuthally varying residual move-out observed on time migrated common image gathers (CIG) definitely disappears on depth migrated CIGs (in both cases no azimuthal anisotropy is introduced in the velocity model). This illustration highlights the limits of the assumptions of time imaging, thus promoting the use of depth imaging when processing high-density WAZ data, even in the context of mild geological complexity.

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Non‐linear tomography for time imaging

Cited by 2 publications

References 7 publications

Can we correct for azimuthal variations of residual move‐out in land WAZ context, using depth non‐linear slope tomography? An imaging case history

Can we correct for azimuthal variations of residual move‐out in land WAZ context, using depth non‐linear slope tomography? An imaging case history

Azimuthal anisotropy? The time and depth imaging points of view: an imaging case history

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