Integrating high resolution resistivity/IP surveying and core measurements over nine known mineral deposits: Yukon, Canada
Notice bibliographique
Résumé
Summary High resolution resistivity/IP (HRRIP) surveys were carried out over nine mineral deposits within Yukon. The objectives of this study were 1) to investigate whether HRRIP surveys could map mineralization and structure associated with known deposits, and 2) to determine which arrays, if any, were the most diagnostic for a given deposit type. The study included a copper porphyry deposit, a lead-zinc-silver vein deposit, and several different styles of gold mineralization/deposits. The location of the HRRIP line(s) were chosen based on the known mineralization and geology of the deposit. On average 3 different arrays were carried out along each line. Variations of dipole-dipole, inverse Schlumberger and pole-dipole arrays, as well as an array developed by Advanced Geosciences Inc. of Austin, Texas were used. Two-dimensional inversions of each array for each line were carried out to produce 2D resistivity and IP cross sections. These sections provided the fundamental information for the study. In addition resistivity and chargeability measurements were carried out on cores from several of the deposits which provided additional information to use when interpreting the sections. The inversion results show that HRRIP surveys are able to produce resistivity and IP sections that generally correlate with the known mineralization and structure of the deposits. However, there are often differences between the inversion results (both resistivity and IP) of the different arrays along a given profile, and the location of resistivity and IP features is sometimes offset or missing relative to known structure and/or mineralization. The depth of penetration of the HRRIP method employed is estimated to be approximately 60 to 70 m for all arrays used except the pole-dipole array which has a depth of penetration estimated to be closer to 90 m. The study shows the HRRIP method can be effective for mapping a range of mineral deposits. However, care must be exercised when interpreting the inversion results because 1) there can be subtleties within the data and 2) not all resistivity and chargeability values from the inversions can be assumed to be associated with mineralization and/or structure. The arrays that were most effective for the HRRIP surveys were the dipole-dipole and inverse Schlumberger arrays, with their variations
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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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».