A new methodology for the acquisition and processing of audio-magnetotelluric (AMT) data in the AMT dead band
Bibliographic record
Abstract
Abstract Distant lightning activity, the natural energy source for the audio-magnetotelluric (AMT) method, has a signal minimum between 1 and 5 kHz, the so-called AMT dead band. The energy in this band exhibits both diurnal and annual variation; magnetic-field amplitudes during the daytime are often well below the noise levels of existing sensors (coil magnetometers), thus reducing the effectiveness of the method for quantitative high-resolution studies of near-surface targets. To overcome this deficiency, we propose a hybrid acquisition and processing methodology based on combining the telluric-telluric (T-T) and telluric-magnetotelluric (T-MT) methods in this frequency range. Our method records the telluric channels at several sites and at base and remote reference stations during the day and records the full magnetotelluric (MT) components at the base and remote stations only during the night. Applying a tensor multiplicative relationship between these responses, we obtain the T-MT AMT transfer functions for the sites; these transfer functions can represent a reasonable approximation of the real AMT impedance tensors. To test the approach, a T-MT experiment was carried out in Sudbury, northern Ontario, during summer 2000. We compare the processed daytime data using the conventional MT approach to those obtained from our T-MT approach. The results demonstrate that our method can determine high-quality estimates in the dead band, although the estimates can be severely affected by noise.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".