Adaptive pre-whiten filtering for the free induction decay transversal signal in weak magnetic detection
Bibliographic record
Abstract
The free induction decay (FID) transversal signal is always employed by a proton precession magnetometer (PPM) to evaluate the time-domain geomagnetic field. Nevertheless, the signal-to-noise ratio (SNR) is an important factor that severely affects the detection accuracy of the magnetic field due to uncontrollable interference sources, including random noise and power frequency noise. In this study, aiming to boost the SNR of the FID transversal signal, a novel filtering algorithm based on a prewhiten (PW) strategy is proposed and the PW filtering was combined with singular value decomposition (SVD) for further noise reduction. This method aims to generate adaptive PW input data before filtering, further decorrelating the noise to reduce the impact of varying noise levels in the received FID signals. The efficiency of the proposed joint filtering framework, dubbed PW-SVD, was evaluated by comparing with two state-of-the-art methods, i.e., SVD and principal component analysis and decomposition, using the same data. The results demonstrated that the proposed PW-SVD method obtained the smallest root mean square error and the highest signal-to-noise ratio improvement among all the compared methods, especially for the strong-noisy scenario, which enhances the environmental adaptability of a PPM.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".