A similarity test for the compartementalization of crystalline rocks into structural domains
Notice bibliographique
Résumé
It is well known that fracture networks play an important role in fluid circulation in crystalline rock mass. Given that crystalline basements have a negligible primary porosity (porosity of the rock matrix) in comparison to their secondary porosity (porosity due to fractures), fracture characterization generally constitute the most important parameter for the determination of the hydraulic characteristics of the rock mass. Fracture characterization may involve fracture samples from different surveying sources such as outcrops, tunnels and boreholes. For a matter of building a conceptual model, for a study area, the geologist compartmentalizes the study area into several structural homogeneous sub-areas. Those homogeneous sub-areas are called structural domains and how fracture samples are grouped in the same structural domain is the question treated in this presentation. From field investigations to grouping fracture samples into structural domains, geologists have used methods that are mainly based on the geologist experience and use major structural elements such as faults as domain boundaries. In the case of total absence or limited presence of major structural elements, grouping fracture samples into structural domains becomes complicated. Therefore, several statistical methods which use fracture characteristics have been developed to assist the geologist for that matter. Those methods can be classified into two approaches, which have been introduced by Miller (1983) and Mahtab and Yegulalp (1984). Miller’s approach consists of grouping fracture samples which are totally homogeneous with regard to the fracture characteristic(s) of interest, especially fracture orientation. On the other hand, Mahtab and Yegulalp’s approach consists of grouping fracture samples which share a similar fracture set. While, Miller’s approach got a lot attention, especially in the engineering fields, Mahtab and Yegulalp’s method has the advantage of allowing taking into consideration the blind zones of fracture samples as in practice a fracture sample can hardly be constituted by all the fracture sets of its belonging structural domain. However, Mahtab and Yegulalps’s method ignore fracture characteristics such as fracture spacing, aperture and persistence which are important for fluid circulation in the rock mass. This presentation proposes a new method that improves Mahtab and Yegulalp’s method by including fracture characteristics such as aperture, persistence and fracture spacing in addition to the fracture orientation considered in the original method. The field investigations took place in the Greenville geological province of the Canadian shield, in Lanaudière region, in Quebec; where fractures were sampled from 30 outcrops and four boreholes. The new method adds a higher level of confidence with regard to the similarity of samples within a structural domain. As a result of the new method, each structural domain has a unique combination of fracture set(s) characteristics which characterize its fracture network. The structural domain compartmentalization impact on the hydrogeological behavior of water flow within the rock mass constitutes the topic of an ongoing research project.
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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 | 0,027 | 0,119 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,009 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,002 | 0,005 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 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 ».