A fast and accurate frequency estimation method for canceling harmonic noise in geophysical records
Bibliographic record
Abstract
Abstract The cancellation of harmonic noise from geophysical records can be achieved by subtracting an estimate of the harmonic noise. Estimating the harmonic noise consists of estimating the fundamental frequency and the amplitudes and phases of all harmonics. We propose a new frequency-estimation method that builds upon the estimator originally proposed by Nyman and Gaiser. This Nyman and Gaiser estimation (NGE) method exploits the fact that the noise fundamental frequency is known to be close to 60 Hz. The NGE method is based on solving a system of four equations that determine the amplitude, phase, and frequency of a given harmonic in the harmonic noise. Hence, NGE can produce frequency estimates for all harmonics. Our improved estimator uses a suitable linear combination of these NGE frequency estimates to produce a more accurate estimate of the fundamental frequency. Our method is more accurate than NGE, and its accuracy is comparable to least-squares estimation (LSE). The advantage of our method is that it is about two times faster than LSE. This speed gain can become valuable when processing large magnetotelluric (MT) data records. Applying our method to the restoration of MT data, we found that the harmonic noise amplitude in the periodogram is reduced by at least 60 dB to a level below that of MT data.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".