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Enregistrement W6992833839

MODELING AND MEASUREMENT OF ATTENUATION IN SYNTHETIC SEISMIC DATASETS

2019· dissertation· en· W6992833839 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2019
Typedissertation
Langueen
DomaineEarth and Planetary Sciences
ThématiqueSeismic Imaging and Inversion Techniques
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAttenuationDeconvolutionReflection (computer programming)Anelastic attenuation factorSynthetic seismogramSeismic waveSeismic to simulationEnergy (signal processing)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Anelasticity and heterogeneity in the Earth decreases the energy and modifies the dominant frequency of seismic wavetrains as they travel through the Earth. These phenomena are known as seismic attenuation. The associated physical processes lead to reduced amplitudes, waveform distortions, and phase delays of seismic wave arrivals. Seismic wave attenuation is often viewed as an important indicator of the presence of fluids, variations of saturation, porosity, and fracturing within the subsurface, as well as of variations of temperature, pressure, and the mineral content of rocks. \nHowever, along with its usefulness for interpreting subtle physical properties of the Earth, seismic wave attenuation is often difficult to measure accurately, and the resulting measures may be difficult to relate to physical properties. In this Thesis, I investigate three types of attenuation-measurement methods in detail, by using three high-quality synthetic datasets. The first dataset simulates a two-dimensional (2-D) reflection seismic profile and is generated by the popular Seismic Un*x software. The second dataset is performed by the classic and accurate one-dimensional (1-D) modeling method called “reflectivity” and simulates the subsurface structure of the Weyburn oil field in southern Saskatchewan. The third synthetic dataset is also 1-D but is unique in modeling a nuclear explosion as the source and covering depths down to about 600 km. These datasets are used to test and compare three methods of attenuation measurement: 1) the well-known spectral ratio (SR) method, 2) the less known instantaneous-frequency matching (IFM) method, and 3) a new method based on time-variant deconvolution (TVD). The TVD method uses the full-waveform modeling for measuring not only the traditional quality factor (usually denoted Q) but also all other effects of attenuation in seismic records, including the effects of reflections, multiples, thin-layer tuning, surface and other types of waves). This method is also the only one allowing measurement of the Q at every point within a seismic section. Due to these properties, the TVD method can be used for advanced interpretation and for compensating the attenuation effects in seismic records. \nWith each of the above methods, detailed Q measurements were performed at variable source-receiver distances for several arrivals within the seismic records and compared to the models. The Q values obtained by the SR, IFM, and TVD methods were found suitable for clear isolated arrivals such as shallow reflections. However, the resulting Q-factors begin deviating from the expected model levels when these arrivals are complicated by interferences with other reflections, multiples, mode conversions, and noise. Because of its spectral averaging properties the SR method is somewhat more stable with respect to such effects. For all three methods, significant variations in performance were found for different source-receiver distances. Overall, the Q-factors measured within the seismic sections are variable and not simply related to the Q of the subsurface. \nA somewhat unexpected yet important result of this study consists in finding that the attenuation modeled by the 2-D Seismic Un*x program is of a very peculiar kind described by the Q-factor proportional to frequency. The above results show that seismic attenuation still requires substantial research in both modeling and measurements, in both exploration-scale and earthquake seismology.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,436
Score d'incertitude au seuil0,977

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,012
Tête enseignante GPT0,170
Écart entre enseignants0,158 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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