The Influence of Spatial Filters on Infrasound Array Responses
Bibliographic record
Abstract
A spatial filter is often attached to a microphone or microbarometer in order to reduce the noise caused by atmospheric turbulence. This filtering technique is based on the assumption that the coherence length of turbulence is smaller than the spatial extent of the filter, and so contributions from turbulence recorded at widely separated ports will tend to cancel while those of the signal of interest, which will have coherence length larger than the spatial dimensions of the filter, will be reinforced. In this paper, the plane wave response for a spatial filter with an arbitrary arrangement of open ports is determined. It is found that propagation over different port-to-sensor distances causes out-of-phase sinusoids to be summed at the central manifold and can lead to significant amplitude decay and phase delays as a function of frequency. The determined spatial filter plane wave response is superimposed on an array response typical of infrasound arrays that constitute the International Monitoring System infrasound network used for nuclear monitoring purposes. It is found that signal detection capability in terms of the Fisher Statistic can be significantly degraded at certain frequencies. The least-squares estimate of signal slowness can change by up to 1.5° and up to 10 m/s if an asymmetric arrangement of low and high frequency spatial filters is used. However, if a symmetric arrangement of filters is used the least-squares estimate of signal slowness is found to be largely unaffected, except near the predicted null frequency.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".