Robust processing of magnetotelluric data in the AMT dead band using the continuous wavelet transform
Bibliographic record
Abstract
Abstract The energy sources for magnetotellurics (MT) at frequencies above 8Hz are electromagnetic waves generated by distant lightning storms propagating globally within the earth-ionosphere waveguide. The nature of the sources and properties of this waveguide display diurnal and seasonal variations that can cause significant signal amplitude attenuation, especially at 1–5kHz frequencies — the so-called audiomagnetotelluric (AMT) dead band. This lack of energy results in unreliable MT response estimates; and, given that in crystalline environments ore bodies located at some 500–1000-m depth are sensed initially by AMT data within the dead band, this leads to poor inherent geometric resolution of target structures. We propose a new time-series processing technique that uses localization properties of the wavelet transform to select the most energetic events. Subsequently, two coherence thresholds and a series of robust weights are implemented to obtain the most reliable MT response estimates. Finally, errors are estimated using a nonparametric jackknife algorithm. We applied this algorithm to AMT data collected in northern Canada. These data were processed previously using traditional robust algorithms and using a telluric-telluric magnetotelluric (TTMT) technique. The results show a significant improvement in estimates for the AMT dead band and permit their quantitative interpretation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".