Geophysical inversion in an integrated exploration program : examples from the San Nicolas deposit
Notice bibliographique
Résumé
The recent ability to produce three-dimensional physical property models of the subsurface from surface geophysical data, coupled with an increasing need to explore for minerals in concealed terranes, results in geophysical inversions providing more significant information to the exploration team. This thesis examines the role that geophysical inversion can play in an integrated mineral exploration program, and the impact it can have on the results. As an example, geophysical data from the San Nicolas copper-zinc massive sulphide deposit in Mexico are inverted. Such deposits are distinguished by high density, magnetic susceptibility, conductivity, and chargeability values. Within the framework of an integrated exploration program (which is communicated through the use of a flowchart) many different, and hard-to-interpret, geophysical data sets are given geologic context. This is achieved by interpreting physical property models that have been generated by inversion modeling. The aim of generating these models, and interpreting geological information from them, is to: 1) assist mineral exploration in the area around the deposit, and roughly define areas of mineralization that can be used as a starting point for more detailed modeling ; 2) define the size and location of the San Nicolas deposit; 3) further improve the models and delineate ore more accurately by combining the detailed modeling with core physical property measurements; and 4) determine how best to use the information that has been acquired through modeling to find additional sulphide ore-bodies, including those that may be deeper than the existing deposit. Density and magnetic susceptibility distribution models, inverted from regional gravity and magnetic data respectively, define large-scale structures that reflect the tectonic setting of the region. Several distinct anomalies that exhibit high density and magnetic susceptibility values are identified. Since massive sulphides are often dense and magnetic, a correlation method is employed that determines volumes that have high density and magnetic susceptibility. The correlation procedure isolates five anomalies. Two of these are easily dismissed as having poor exploration potential, and two of the remaining anomalies, one of which is the San Nicolas deposit, are associated with mineralization. At a more detailed scale, the deposit is well defined by gravity, magnetic, CSAMT, and IP methods individually. However, a drill hole that is targeted on the intersection of these favorable physical property distributions would have intersected the heart of the deposit. This demonstrates the advantages of using these methods in concert. Other sources of information, such as core physical property measurements and geologic constraints, are also used to improve modeling results. The inclusion of data from a single drill-hole is shown to significantly enhance detailed physical property distributions, and produces models that correlate better which known mineralization. Finally, forward modeling of the physical property models is used to demonstrate how deep ore can be detected by implementing different survey designs that increase signalto- noise of the data. This newly acquired information can then be used in the next step of the exploration program as the search for mineralization continues i n the surrounding area. In addition to the main focus of the thesis, it was found that polarizable material is needed in order to fit time-domain, airborne electromagnetic data collected over the deposit. This demonstrates the potential for detecting chargeable bodies from the air, and has significant implications for future mineral exploration.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».