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Enregistrement W2116833165 · doi:10.2172/945944

Feature Detection, Characterization and Confirmation Methodology: Final Report

2007· report· en· W2116833165 sur OpenAlexaboutno aff
Kenzi Karasaki, John Apps, Christine Doughty, Hope Gwatney, C. T. Onishi, Robert Trautz, C.M. Tsang

Notice bibliographique

Revuenon disponible
Typereport
Langueen
DomaineEarth and Planetary Sciences
ThématiqueSeismic Imaging and Inversion Techniques
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésFeature (linguistics)Characterization (materials science)GeologyGeographyComputer science

Résumé

récupéré en direct d'OpenAlex

This is the final report of the NUMO-LBNL collaborative project: Feature Detection, Characterization and Confirmation Methodology under NUMO-DOE/LBNL collaboration agreement, the task description of which can be found in the Appendix. We examine site characterization projects from several sites in the world. The list includes Yucca Mountain in the USA, Tono and Horonobe in Japan, AECL in Canada, sites in Sweden, and Olkiluoto in Finland. We identify important geologic features and parameters common to most (or all) sites to provide useful information for future repository siting activity. At first glance, one could question whether there was any commonality among the sites, which are in different rock types at different locations. For example, the planned Yucca Mountain site is a dry repository in unsaturated tuff, whereas the Swedish sites are situated in saturated granite. However, the study concludes that indeed there are a number of important common features and parameters among all the sites--namely, (1) fault properties, (2) fracture-matrix interaction (3) groundwater flux, (4) boundary conditions, and (5) the permeability and porosity of the materials. We list the lessons learned from the Yucca Mountain Project and other site characterization programs. Most programs have by and large been quite successful. Nonetheless, there are definitely 'should-haves' and 'could-haves', or lessons to be learned, in all these programs. Although each site characterization program has some unique aspects, we believe that these crosscutting lessons can be very useful for future site investigations to be conducted in Japan. One of the most common lessons learned is that a repository program should allow for flexibility, in both schedule and approach. We examine field investigation technologies used to collect site characterization data in the field. An extensive list of existing field technologies is presented, with some discussion on usage and limitations. Many of the technologies on the list were in fact used during the characterization of Yucca Mountain and elsewhere by LBNL personnel. The study also includes emerging technologies and identifies the need to develop better estimation of important parameters for repository siting. Notable emerging technologies include 3-D seismic and satellite-based remote sensing and wireless micro electro mechanical systems (MEMS) sensors. They enable cost-effective and ubiquitous monitoring to be applied for site characterization. We list and classify the types of uncertainties involved in site characterization. Uncertainties can exist in all aspects of site characterization: data, interpretation, conceptualization, and modeling. We use the Swedish program to exemplify such uncertainties. We also devote a chapter on geochemical issues regarding the interaction between groundwater and natural and engineered barrier materials. A recommendation has been made to take advantage of the recent advancement in geochemical modeling capabilities in natural systems. Although it is not of immediate relevance at the preliminary investigation stage, it serves as a good reminder that geochemical investigation efforts should not be overlooked at any stage in the repository program. We construct a synthetic preliminary-investigation site based on an extensive data set available from a geoscientific project in Japan, which we use as a 'real' site to evaluate uncertainties resulting from hydrogeological modeling and examine strategies for characterizing a new site. We plan various preliminary-investigation configurations and conduct preliminary numerical investigations at the synthetic site. We construct a model of the 'real' site for each PI configuration, make predictions of particle travel times, and compare against the 'real' data obtained from the 'real' model. We conclude that drilling as many as nine boreholes does not necessarily improve the understanding of the site compared to drilling as few as three boreholes, unless there is an underlying structure that is larger than the spacing of the boreholes. The parameters that affect the outcome of the predictions most are: (1) effective porosity, (2) boundary conditions, and (3) fault properties, all of which are very difficult to estimate in the field and are full of uncertainties. Of the three, we recommend NUMO expend efforts to assess the latter two at preliminary investigation sites. To obtain large-scale averaged permeabilities, we recommend conducting long-time and long-interval pumping tests in boreholes. We also find that the temperature data can reduce some uncertainties regarding the boundary conditions. Finally, we summarize recommendations that NUMO might consider during preliminary site investigations.

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)0,041
score de la tête « metaresearch » (Gemma)0,104
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,111
Score d'incertitude au seuil0,372

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0410,104
Méta-épidémiologie (sens strict)0,0030,002
Méta-épidémiologie (sens large)0,0010,003
Bibliométrie0,0060,003
Études des sciences et des technologies0,0020,001
Communication savante0,0070,005
Science ouverte0,0040,005
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,1110,133

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 Opus0,094
Tête enseignante GPT0,312
Écart entre enseignants0,218 · 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'étudeSans objet
Domainenon disponible
GenreAutre

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
Publié2007
Routes d'admission1
Résumé présentoui

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