Imaging of the subsurface magnetization of the Krafla geothermal area using a high-resolution drone magnetic survey and constrains from a 3D electrical conductivity model
Notice bibliographique
Résumé
During the summers of 2021 and 2022, we conducted drone magnetic surveys over the Krafla geothermal system in the Northern Volcanic Zone of Iceland. The purpose of this survey was to image the subsurface magnetization to help characterize the geometry of the geothermal system and to determine the geological structures and lithologies controlling it. This new survey was collected with two types of magnetometer systems (a fluxgate vector system and a cesium scalar system) fixed to a hexacopter and flown over an area of about 20 km2 with a spatial resolution (i.e. flight line spacing and flight elevation above ground level) of 50 m. The data were corrected for the magnetic effect of the drone using the MagComPy software of Kaub et al. (Geochem. Geophys. Geosyst., 22, e2021GC009745, 2021), for the diurnal variations of the Earth’s magnetic field using a local base-station magnetometer, and for the main (large-scale) magnetic field using the IGRF (International Geomagnetic Reference Model) model. The resulting magnetic anomaly map exhibits a pronounced magnetic low coincident with the active geothermal system. The map also displays many remarkable short-wavelength anomalies associated with topography, cultural features, geological structures such as fault and fissures, areas of superficial hydrothermal alteration and recent lava flows. The comparison of observed and terrain anomalies, the latter computed assuming a constant magnetization of about 10 A/m below topography, suggests a strong influence of topography. However, many discrepancies between observed and terrain anomalies also indicate significant variations of magnetization in the subsurface. We then tested whether we can assume that the main source of rock magnetization variations is a demagnetization associated with hydrothermal processes in the geothermal reservoir. To this end, we used the 3D model of electrical conductivity from Lee et al. (Geophys. J. Int., 220, 541-567, 2020) to evaluate the depth to the top of the geothermal reservoir, characterized by a high conductivity layer interpreted as a clay cap. Magnetic anomalies were then predicted assuming a simple forward model with constant and null magnetization above and below the clay cap, respectively. The resulting predicted anomalies reproduce some large scale features from the observed anomaly map but also display significant differences especially for short-wavelength signals. We therefore inverted for the distribution of magnetization in rocks above the geothermal reservoir using the jif3D code of Moorkamp et al. (Geophys. J. Int., 184, 477-493, 2011) and imposing a null magnetization in the reservoir. The resulting distribution of magnetization appears to be strongly influenced by the distribution of surface alteration and fresh recent lava flows that were not accounted for in our initial forward model due to both the simplicity of the modeling assumptions and the lower spatial resolution of the electrical conductivity model. This study suggests that the joint inversion of magnetic and electrical conductivity data is a promising approach for the imaging of geothermal systems as it takes advantage of both the sensitivity with depth of electromagnetic methods and the lateral sensitivity of high-resolution magnetic surveys.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| 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 | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».