A similarity test for the compartementalization of crystalline rocks into structural domains
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".