Uncertainty Modeling of a Vein Deposit
Notice bibliographique
Résumé
This thesis sets out a workflow to quantify uncertainty of the Arrow Deposit, a tabular, vein type, high-grade, basement-hosted, uranium deposit located on NexGen Energy Limited’s 100%-owned Rook I property in northern Saskatchewan. The uncertainty associated with the volume, grade, and density variables of the deposit are the focus of the study, as these variables define the overall metal content of the deposit, the largest input to project economics. In the study domain, the grade variable is exhaustively sampled at all drill hole locations, but the density variable is missing at approximately 82% of the drill hole locations and biased to high-grade intercepts. Effort was taken to make an interim debiased homotopic representative dataset through a simple imputation process that selected density values from the global deposit-wide dataset. Two uncertainty models were created for the grade and density variables using the representative dataset: a multivariate spatial bootstrap model and a density-imputed model using a Gaussian Mixture Model built on the representative dataset, followed by decorrelation via Projection Pursuit Multivariate Transformation and independent grid Sequential Gaussian Simulation. The multivariate spatial bootstrap model provided an assessment of uncertainty of the histogram parameters while considering the spatial correlation between the variables at data locations. The imputation and simulation process features the transfer of the uncertainty associated with the missing values of the original dataset while accounting for the biased nature of the dataset. Volume uncertainty was quantified by assessing: the uncertainty of the thickness perpendicular to the plane of continuity via a geometry imputation process, and the uncertainty of the boundary in the plane of continuity via an indicator estimate. The two assessments of domain uncertainty were combined to output volume uncertainty. The volume, grade, and density uncertainty models were combined into a single model; this uncertainty model will be used as the basis to evaluate mine output uncertainty with the mine design as the transfer function. The metal content of the Arrow Deposit is most sensitive to changes in volume due to the extreme high-grade nature of the deposit, therefore the Boundary model, which has a large range of possible volumes (and range in the determination of ore/not ore), contributes the most to the metal content uncertainty. The Grade/Density model also significantly contributed to the metal content uncertainty, which is credited to the short-range variability of the variables. The thickness perpendicular to the plane of continuity model provided additional uncertainty to the metal content, but to a much lesser degree as the range of possible volumes associated with the model were relatively small.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
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 tête enseignante, 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 ».