Deep Learning-Based Denoising of Noisy Vibration Signals from Wavefront Sensors Using BiL-DCAE
Bibliographic record
Abstract
In geophysical exploration, laser remote sensing detection of seismic waves based on wavefront sensors can be used for geological detection and geophysical exploration. However, due to the high sensitivity of the wavefront sensor, it is easy to be affected by the environmental light and vibration, resulting in random noise, which is difficult to predict, thus significantly reducing the quality of the vibration signal and the detection accuracy. In this paper, a large amount of data is collected through a single-point vibration detection experiment, and the relationship between amplitude and spot centroid offset is analyzed and calculated. The real noisy vibration signal is denoised and signal enhanced by using a BiLSTM denoising convolutional self-encoder (BiL-DCAE). The irregular and unpredictable noise generated by various complex noise mixing is successfully suppressed, and its impact on the vibration signal is reduced. The signal-to-noise ratio of the signal is increased by 13.90 dB on average, and the noise power is reduced by 95.93%, which greatly improves the detection accuracy.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".