Application of multiple-point simulation of mineral deposits based on discrete wavelet transform
Notice bibliographique
Résumé
Traditionally, geostatistical simulations of mineral deposits are done by using methods based on two-point spatial statistics, such as variograms.However, second-order spatial statistics are not sufficient to capture critical and common features from mineral deposits, such as connectivity of extreme values and curvilinear patterns, which in turn drive a mine production sequence.As a way to overcome these important limitations, multiple-point simulation (MPS) methods have been developed.In this thesis, a MPS method based on wavelet analysis and on pattern recognition is described in detail.The idea in wavelet analysis applied to image compression is to decompose an image in two types of information: 1) average type information of the nearby pixels, called approximate sub-band of the image; and 2) how these pixels depart from local average.Usually, the sub-band image is a sufficient representation of the whole image, and can be used for several applications instead of the whole image.The simulation method used herein works as follows: first, it scans a training image with a template to generate a pattern database; then this database has its dimension reduced by applying discrete wavelet transform, so the approximate sub-band image of the patterns are obtained; after that, the patterns are divided into classes using k-means clustering algorithm, considering the approximate sub-band image; finally, the grid is simulated by comparing the conditioning data event in each node with the classes prototypes and choosing a pattern from that class.The practical intricacies and the contributions of this approach are tested and analyzed through an application at Olympic Dam copper deposit, which is located in South Australia and is the fourth largest producer of this commodity in the world.Both material types and copper grades were simulated in this case study.The categorical training image is generated through geological interpretation, and the continuous training image is generated by using low-rank tensor completion.Olympic Dam's simulation results show that the method used herein can be applied successfully to relatively complex and large deposits.Additionally, the results suggest that care must be taken when generating the training image, since it plays a very important role in the simulation process.The resulting simulated realizations are analyzed and validated in terms of histograms, vi variograms and high-order statistics, the latter being performed by using high-order spatial cumulants.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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) | 0,000 | 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 ».