Microphone experiments and applications in exploration seismology
Bibliographic record
Abstract
Coupling phenomena associated with energy conversion at the air-ground interface can be better understood if pressure levels and particle velocity, displacement or acceleration amplitudes are recorded in the field. In exploration seismology, the air pressure — ground motion relationship is essential to understand the coupling mechanism of air-associated noise into the geophones. The CREWES Project at the University of Calgary undertook two air-pressure recording experiments in western Canada to investigate using air-pressure data (from microphones) to attenuate air-coupled noise in geophones during two different seismic acquisition projects. A multichannel median filter was applied to the LMO-corrected microphone data to enhance the strong air blast arrival and allowed us to study our recorded data in terms of sound propagation and attenuation, power spectra and signal consistency. Adaptive filtering techniques produced reasonable estimates of the embedded geophone noise using a reference noise input (i.e. pressure data from several microphones). We had success in suppressing the 60 Hz interference by using the Least-Mean Squares (LMS) algorithm in a Finite-Impulse Response adaptive filter. The results are quite encouraging and more complex adaptive filter algorithms and other filtering techniques are under study.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".