Determination of orientation of marine magnetometers by means of modelled local field variations derived from spherical elementary current systems
Bibliographic record
Abstract
In the marine environment, active and passive electromagnetic (EM) measurements are used to derive information about the conductivity structure beneath the seafloor. While the conductivity is mostly determined by the conductive seawater contained in the pore space or fractures, anomalies may occur in the presence of more resistive (e.g. hydrocarbons, free gas, gas hydrates, freshened water) or conductive materials (e.g. massive sulfides, brines). For the correct interpretation of EM data it is important to know the measurement geometry, including the orientations of receivers. From an experimental standpoint, this can be challenging because stations are often deployed free falling, thus, ending up in arbitrary orientations on the seafloor. The orientations are frequently derived from electronic compass measurements or magnetometers which record all components of the magnetic field. However, these measurements may be distorted by magnetic parts on stations (e.g. batteries), biased by local inhomogeneities in the local field or difficult to perform if no reliable reference data from a nearby observatory is available.A possible remedy for such problems may come from space physics. Given a grid of stationary magnetometer stations, surrounding the area of interest but at relatively large distances, the method of spherical elementary current systems (SECS) (Amm & Viljanen, 1999, Earth, Plants and Space) can be used to reconstruct equivalent ionospheric currents and their resulting time variations of the magnetic field at any point within the grid. The method is especially suitable to be applied at northern latitudes, where fairly dense magnetometer networks such as IMAGE and CARISMA exist, and where the magnitude of geomagnetic disturbances from ionospheric currents is significant.We have successfully applied the method to three marine EM data sets, one offshore Iceland, one in the arctic section of the North Atlantic (Loki's Castle) and one off the Canadian coast (Prince Edward Island). The SECS method qualitatively reproduces the magnetic variation as observed by the seafloor stations. Here we investigate the results from the above mentioned EM data sets, and discuss the applicability, accuracy and constraints of the SECS method for EM data calibrations. Furthermore, we illuminate the possibility for using SECS as an interpolation tool for other applications at remote offshore locations, such as measurement while drilling (MWD) operations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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".