Reducing Spatial Aliasing in Wave-Equation Multiple Attenuation
Notice bibliographique
Résumé
Abstract Multiples are a major problem in offshore seismic exploration. Wave-equation methods are a popular tool for multiple attenuation, but behave poorly in the presence of spatial aliasing. This paper will propose a method to reduce the effect of spatial aliasing. It does this by first applying a linear transformation to the wave equation. This transformation reduces dips in the data, but does not involve approximations, nor does it change the form of the equation. Wave extrapolation is then performed using an efficient Kirchhoffstyle phase-shift operation. The modeled multiples are adaptively subtracted from the original traces with a multichannel constrained filter. The proposed method has givengood results on a data set from the East Coast of Canada. Introduction The basic model in seismic processing assumes that reflection data consists of primaries only. If multiples are not removed they can be misinterpreted as, or interfere with, primaries. Therefore the attenuation of multiple reflections has been an important research subject and many methods have been developed. The methods can be classified into two types: those that exploit the periodic nature of multiples, and those that exploit the moveout differences between multiple and primary reflections. The first category includes predictive deconvolution in the x-t or ?-? domain. Predictive deconvolution assumes that the multiples are periodic within an application window and that the primary events are not - that is, the multiples can be predicted and thereby subtracted from the seismic records. In the prestack x-t domain, water bottom multiples are periodic only for zero offset traces and flat-water bottoms. To achieve periodicity of multiples for far-offset traces, transforms such as the ?-? transform or the radial trace method must be applied. Still, for shallow water depths (shorter period multiples), periodicity only holds in a limited window. Predictive deconvolution can introduce spurious events and needs to be applied with care. It is more effective for removing "ringing" multiple energy than longer period waterbottom multiples. The second category includes methods in the F-K, K-L and ?-? domains. While these methods are routinely used and can be very effective, there have a number of problems. All of these methods lose effectiveness when the velocity discrimination between primary events and multiples is small. This is the case at near offsets where there is little or no separation of primary and multiple events, and at all offsets when there is little velocity difference. Their effectiveness is further reduced when the seismic events violate the modeling assumptions (non-hyperbolic events for F-K and K-L, or non-parabolic events after NMO for ?-?). Recently there has been interest in techniques based on wave theory, which can be considered extensions of conventional predictive deconvolution. Multiples are predicted by either wavefield extrapolation1,2,3 or wavefield inversion4. They are then subtracted from the original data.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».