Cancellation of multiple harmonic noise series in geophysical records
Bibliographic record
Abstract
Abstract We describe a procedure for the simultaneous estimation of multiple stationary sinusoidal contaminants in a time series and demonstrate its application to the cancellation of powerline noise in seismic and seismoelectric records. An estimate of the noise in each record is obtained by seeking a linear combination of sinusoids that are harmonics of one or more fundamental frequencies; this combination must fit the data in a least-squares sense. The algorithm accommodates estimation windows of arbitrary length, including windows shorter than one fundamental period, without cross-contamination between harmonic estimates. Provision is made for refining the fundamental frequencies, for which small errors (e.g., 0.01 Hz in 60 Hz) can result in significant residual noise near the ends of the record (beating). Our strategy is novel in that it uses all specified harmonics and iteratively searches frequency space for a best fit using numerical derivatives. This determination is very robust for at least two fundamentals (the limit of our investigation) and often converges rapidly to three or four decimal places. Cancellation of harmonic noise has been essential in our research to uncover seismoelectric signals that are often completely masked by powerline noise. We expect this procedure will be useful in other geophysical methods—especially exploration seismology, for which powerline contamination is a recognized problem.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".