Airborne natural source electromagnetics for an arbitrary base station
Bibliographic record
Abstract
SUMMARY Airborne magnetotelluric (AirMT) systems generate transfer function data from magnetic fields measured in the air and either electric or magnetic fields measured at a base station. AirMT anomalies are fundamentally controlled by the anomalous magnetic fields within the survey region. While AirMT data acquired using a magnetic field base station are not directly sensitive to the conductivity at the base station, AirMT data acquired using an electric field base station are scaled by the inverse square root of the conductivity at the base station. The transfer function data collected by various AirMT systems have different sensitivity functions. Consequently, the inversion of AirMT data for different acquisition systems may not recover the same conductivity model for the same set of inversion parameters. In this paper, we aim to characterize the fundamental similarities and differences between AirMT inversion for data collected using a magnetic field base station, and for data collected using an electric field base station. We adopt an unconstrained, smoothest model inversion approach to characterize the structures that are naturally recovered by inverting AirMT data when the base station is far away and located on the surface of a homogeneous quarter-space. Our work shows that when a-priori knowledge of the host conductivity within the survey region is available, AirMT inversion effectively recovers conductive and resistive structures within the survey region, regardless of whether the data are collected using an electric or magnetic base station. We show that a single ground magnetotelluric station might provide enough information about the host conductivity to construct a suitable starting model for AirMT inversion, and we discuss the impact of jointly inverting AirMT data and ground magnetotelluric data for a single station.
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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 | 0.000 | 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) | 0.003 | 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".