Stereotomographic picking in practice

Lambaré, Gilles; Alerini, M.; Podvin, Pascal

doi:10.1190/1.1851227

Cited by 14 publications

(8 citation statements)

References 8 publications

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“…Furthermore, locally coherent events corresponding to 'frowning' events in common image gathers computed by kinematic ray+Born migration/inversion below the gas layers were identified as internal multiples from the gas layers, and therefore removed (Lambaré et al 2004b;Lambaré and Alérini 2005). The horizontal spacing between the B-spline knots is kept at 1000 m, as the medium is rather tabular.…”

Section: Stereotomographic Inversion Setupmentioning

confidence: 99%

“…The data are computed with a Dirac wavelet band-pass filtered between 10-45 Hz with a Butterworth filter. Before picking, we apply an automatic gain control (AGC) to the data, with an AGC window length of 0.5 s, for easing the picking of the late-arriving phases from the deep part of the target (Lambaré, Alerini and Podvin 2004b). We apply an external mute to remove any numerical noise before the first arrival and an internal mute after the reflection from the deep reflector located at 5000 m in depth to remove late arrivals, which are not associated with single-scattered phases (Fig.…”

Section: Stereotomography Data Preprocessingmentioning

confidence: 99%

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Building starting models for full waveform inversion from wide‐aperture data by stereotomography

Prieux

Lambaré

Operto

et al. 2012

Geophysical Prospecting

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Building an accurate initial velocity model for full waveform inversion (FWI) is a key issue to guarantee convergence of full waveform inversion towards the global minimum of a misfit function. In this study, we assess joint refraction and reflection stereotomography as a tool to build a reliable starting model for frequency‐domain full waveform inversion from long‐offset (i.e., wide‐aperture) data. Stereotomography is a slope tomographic method that is based on the inversion of traveltimes and slopes of locally‐coherent events in a data cube. One advantage of stereotomography compared to conventional traveltime reflection tomography is the semi‐automatic picking procedure of locally‐coherent events, which is easier than the picking of continuous events, and can lead to a higher density of picks. While conventional applications of stereotomography only consider short‐offset reflected waves, we assess the benefits provided by the joint inversion of reflected and refracted arrivals. Introduction of the refracted waves allows the construction of a starting model that kinematically fits the first arrivals, a necessary requirement for full waveform inversion. In a similar way to frequency‐domain full waveform inversion, we design a multiscale approach of stereotomography, which proceeds hierarchically from the wide‐aperture to the short‐aperture components of the data, to reduce the non‐linearity of the stereotomographic inversion of long‐offset data. This workflow which combines stereotomography and full waveform inversion, is applied to synthetic and real data case studies for the Valhall oil‐field target. The synthetic results show that the joint refraction and reflection stereotomography for a 24‐km maximum offset data set provides a more reliable initial model for full waveform inversion than reflection stereotomography performed for a 4‐km maximum offset data set, in particular in low‐velocity gas layers and in the deep part of a structure below a reservoir. Application of joint stereotomography, full waveform inversion and reverse‐time migration to real data reveals that the FWI models and the reverse‐time migration images computed from the stereotomography model shares several features with FWI velocity models and migrated images computed from an anisotropic reflection‐traveltime tomography model, although stereotomography was performed in the isotropic approximation. Implementation of anisotropy in joint refraction and reflection stereotomography of long‐offset data is a key issue to further improve the accuracy of the method.

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Section: Stereotomographic Inversion Setupmentioning

confidence: 99%

Section: Stereotomography Data Preprocessingmentioning

confidence: 99%

Building starting models for full waveform inversion from wide‐aperture data by stereotomography

Prieux

Lambaré

Operto

et al. 2012

Geophysical Prospecting

Self Cite

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“…The two steps can be repeated when the velocity model gets refined by imaging. Picking seismic velocities remains one of the most labor-intensive and time-consuming tasks in seismic data processing although several approaches have been developed to partially automate and simplify it Lambaré et al, 2004b). More than just a single parameter (such as seismic velocity) needs to be picked to use higher-order traveltime approximations such as those suggested by the multifocusing approach .…”

Section: Introductionmentioning

confidence: 99%

Velocity‐independent time‐domain seismic imaging using local event slopes

Fomel

2005

SEG Technical Program Expanded Abstracts 2005

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By estimating local event slopes in prestack seismic reflection data, it is possible to accomplish all common time-domain imaging tasks, from normal moveout to prestack time migration, without the need to estimate seismic velocities or any other attributes. Local slopes contain complete information about the reflection geometry. Once they are estimated, seismic velocities and all other moveout parameters turn into data attributes and are directly mappable from the prestack data domain into the time-migrated image domain. I develop an analytical theory for this method and demonstrate its applicability on synthetic and field data examples.

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“…This velocity-picking * E-mail: guochang.liu@cup.edu.cn process requires a great deal of manual work and is also prone to human error. The automatic velocity analysis methods that calculate and pick stacking velocity from semblance scans have been developed (Adler and Brandwood 1999;Siliqi et al 2003;Lambaré, Alerini and Podvin 2004). Toldi (1989) proposes to estimate stacking velocity by possible interval velocity.…”

Section: Introductionmentioning

confidence: 99%

“…Toldi (1989) proposes to estimate stacking velocity by possible interval velocity. The automatic velocity analysis methods that calculate and pick stacking velocity from semblance scans have been developed (Adler and Brandwood 1999;Siliqi et al 2003;Lambaré, Alerini and Podvin 2004). Weighted semblance is also proposed in order to help distinguish peaks in the velocity spectrum.…”

Section: Introductionmentioning

confidence: 99%

Automatic stacking‐velocity estimation using similarity‐weighted clustering

Liu

et al. 2017

Geophysical Prospecting

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Local seismic event slopes contain subsurface velocity information and can be used to estimate seismic stacking velocity. In this paper, we propose a novel approach to estimate the stacking velocity automatically from seismic reflection data using similarity‐weighted k‐means clustering, in which the weights are local similarity between each trace in common midpoint gather and a reference trace. Local similarity reflects the local signal‐to‐noise ratio in common midpoint gather. We select the data points with high signal‐to‐noise ratio to be used in the velocity estimation with large weights in mapped traveltime and velocity domain by similarity‐weighted k‐means clustering with thresholding. By using weighted k‐means clustering, we make clustering centroids closer to those data points with large weights, which are more reliable and have higher signal‐to‐noise ratio. The interpolation is used to obtain the whole velocity volume after we have got velocity points calculated by weighted k‐means clustering. Using the proposed method, one obtains a more accurate estimate of the stacking velocity because the similarity‐based weighting in clustering takes into account the signal‐to‐noise ratio and reliability of different data points in mapped traveltime and velocity domain. In order to demonstrate that, we apply the proposed method to synthetic and field data examples, and the resulting images are of higher quality when compared with the ones obtained using existing methods.

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Stereotomographic picking in practice

Cited by 14 publications

References 8 publications

Building starting models for full waveform inversion from wide‐aperture data by stereotomography

Building starting models for full waveform inversion from wide‐aperture data by stereotomography

Velocity‐independent time‐domain seismic imaging using local event slopes

Automatic stacking‐velocity estimation using similarity‐weighted clustering

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