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Enregistrement W2582191904

Non-linear Bayesian inversion of controlled source electromagnetic data offshore Vancouver Island, Canada, and in the German North Sea

2014· dissertation· en· W2582191904 sur OpenAlexaboutno aff
Romina Gehrmann

Notice bibliographique

RevueePrints Soton (University of Southampton) · 2014
Typedissertation
Langueen
DomaineEarth and Planetary Sciences
ThématiqueSeismic Imaging and Inversion Techniques
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGermanSubmarine pipelineInversion (geology)OceanographyBayesian probabilityGeographyGeologySeismologyComputer scienceArchaeologyArtificial intelligence
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This thesis examines the sensitivity of the marine controlled source electromagnetic (CSEM) method to sub-seafloor resistivity structure, with a focus on gas hydrate and free gas occurrences. Different analysis techniques are applied with progressive sophistication to a series of studies based on simulated and measured data sets. CSEM data are modelled in time domain for one-dimensional models with gas hydrate, free gas and/or permafrost occurrences. Linearized and non-linear inversion methods are considered to infer subsurface models from CSEM data. One study applies forward modelling and singular value decomposition to estimate uncertainties for permafrost models of the Beaufort Sea. This simulation study analyzes the resolution of the CSEM data for shallow water depth which is a challenging case because the electromagnetic signature of the air-water boundary may mask the sub-seafloor response. The results reveal a blind window as a function of water depth in which the CSEM data are insensitive to the sub-seafloor structure. However, the CSEM data are sensitive to the top and the bottom of the permafrost with increasing uncertainties with depth. The next study applies non-linear Bayesian inversion to CSEM data acquired in 2005/2006 on the Northern Cascadia margin to investigate sub-seafloor resistivity structure related to gas hydrate deposits and cold vents. Bayesian inversion provides a rigorous approach to estimate model parameters and uncertainties by probabilistically sampling of the parameter space. The resulting probability density function is interpreted here in terms of posterior median models, marginal and joint marginal probability densities for model parameters and credibility intervals. The Bayesian information criterion is applied to determine the amount of structure (number of layers) that can be resolved by the data. The parameter space is sampled with the Metropolis-Hastings algorithm in principal-component space. Non-linear, probabilistic inversion allows the analysis of unknown acquisition parameters such as time delays between receiver and transmitter clocks or unknown source amplitude. The estimated posterior median models and credibility intervals from Bayesian CSEM inversion are compared to reflection seismic data to provide a more complete geological interpretation. The CSEM data on the Northern Cascadia margin generally reveal a 1 to 3 layer sediment structure. Inversion results at the landward edge of the gas hydrate stability zone indicate a sediment unconformity as well as several potential cold vents which were previously unknown. The resistivities generally increase upslope due to sediment erosion along the slope. Inversion results on the middle slope infer several vent systems close to well-known Bullseye vent in agreement with ongoing interdisciplinary observations. Finally, a trans-dimensional (trans-D) Bayesian inversion is applied to CSEM data acquired in 2012 in the German North Sea to investigate possible free gas occurrences. Trans-D inversion treats the number of layers as an additional unknown sampled probabilistically in the inversion. %over the parameter space by evaluating probabilistically the transition to a higher or lower number of interfaces. Parallel tempering is applied to increase sampling efficiency and completeness. Inversion results for the German North Sea yield resistivities at the seafloor which are typical for marine deposits, while resistivities at greater depth increase slightly and can be correlated with a transition from fine-grained marine deposits (Holocene age) to coarse-grained, glacial sediments (Pleistocene age), which is observed in a sediment core. The depths of layer interfaces estimated from CSEM inversion match the seismic reflector related to the contrast between the two depositional environments. The CSEM survey targeted a strong, phase-reversed, inclined seismic reflector within the glacial sediments, potentially indicating free gas. While interface-depth estimates from CSEM inversion do not correlate closely with this reflector, resistivities are generally elevated above the strong seismic amplitudes and the thickness of the resistive layer follows the trend of the inclined reflector. However, the uncertainties of deeper interface depth estimates increase significantly and overlap with the targeted reflector at some of the measurement sites. Relatively low resistivities of a third layer correlate with sediments of late-Miocene origin with a high gamma-ray count indicating an increased amount of fine-grained sediments with organic material. The interface at the bottom of the third layer has wide uncertainties which relates to the penetration limit of the CSEM array.

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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,038
Score d'incertitude au seuil0,076

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,007
Tête enseignante GPT0,192
Écart entre enseignants0,185 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
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
Publié2014
Routes d'admission1
Résumé présentnon

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