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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".