Approbation and Implementation of New Technologies for Processing Seismic Data of Complex Folded Zones (CRS, Beam, RTM)
Notice bibliographique
Résumé
Abstract In complex folded areas with harsh tectonic conditions, there are problems in the seismic imaging of the subthrust and areas with steep slope angles. Recently, new seismic processing migration algorithms have appeared, which are quite expensive in terms of computational resources, but on the other hand, they make it possible to display complex structures more correctly, for example, with salt-dome tectonics. The purpose of this work is to test new algorithms for improving the signal-to-noise ratio in areas with complex wave fields and migrations. Nine 2D seismic lines were processed in the Omega 2018 software package using CRS, Beam, and RTM technologies. The pre-processing stage included quality analysis of seismic signal and estimation for sources/receivers such parameters as: schematic maps of root-mean-square amplitudes, dominant frequencies, and signal-to-noise ratios. The r robust Surface-consistent deconvolution was applied to improve the signal processing. After selecting the optimal parameters, CRS summation and CRS seismogram operators were obtained. The RTM migration used TEEC ware's RTM method, which leads to migration using relief and generates CRP gathers in either the surface offset region or the reflection angle region. Angular seismograms were calculated to improve the signal-to-noise ratio. Another measure to ensure a high signal-to-noise ratio was the use of CRS gathers as input data, which greatly improved depth imaging. Simulation software for migration processing was used for deep migration of CRS seismograms. The cluster-based imaging system generates seismograms with normal reflection angles without azimuth dependence. Although the velocity models for Beam and RTM are equal, the speed models for PSTM and RTM are very different. When forming a deep velocity model for PSTM, it is necessary to perform smoothing on a large base, otherwise migration artifacts will arise in places of sharp changes in velocities. For RTM, on the contrary, a correct speed model is required (without anti-aliasing), which will generate an image of higher quality. Beam migration calculations are higher than Kirchhoff migration due to more correct consideration of the dynamics and path of rays. However, these calculations are not comparable to the per-account costs for RTM. RTM migration costs are also very sensitive to the maximum frequency for calculations. RTM migration produces less noise than Beam migration and performs better in areas where reflections are lost. However, one must keep in mind that the speeds for Beam and RTM migrations, although the same, were obtained using RTM migration and if only Beam migration was used, the result could be worse. In different parts of the section, the advantages of one or another migration are visible. In general, it is noticeable that RTM migration works better in the upper part of the sections. The frequency content of the RTM migration recording is often higher and there is less noise. The methods outlined in this paper will reduce problems in imaging the environment in subthrust parts of structures and areas with steep slope angles, which is actual problem for Caspian, Russian and some parts of Middle East regions.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».