Infrasound Signal Detection and Back Azimuth Estimation Using Ground‐Coupled Airwaves on a Seismo‐Acoustic Sensor Pair
Bibliographic record
Abstract
Abstract We present a new infrasonic signal detection and back azimuth determination technique that requires just one microphone and one three‐component seismometer. Ground‐coupled airwaves (GCAs) occur when an incident atmospheric acoustic wave impinges on the ground surface and is partially transmitted as a seismic wave. GCAs are commonly detected hundreds of kilometers away on seismic networks and are observed to have retrograde particle motion. Horizontally propagating acoustic waves and GCAs have previously been observed on collocated infrasound and seismic sensor pairs as coherent with a 90° phase difference. If the sensors are spatially separated, an additional propagation‐induced phase shift is present. The additional phase shift depends on the direction from which the acoustic wave arrives, as each back azimuth has a different apparent distance between the sensors. We use the additional phase shift, the coherence, and the characteristic particle motion on the three‐component seismometer to determine GCA arrivals and their unique back azimuth. We test this technique with synthetic seismo‐acoustic data generated by a coupled Earth‐atmosphere 3‐D finite difference code, as well as three seismo‐acoustic data sets from Mount St. Helens, Mount Cleveland, and Mount Pagan volcanoes. Results from our technique compare favorably with traditional infrasound array processing and provide robust GCA detection and back azimuth determination. Assuming adequate station spacing and sampling, our technique provides a new and robust method to detect infrasonic signals and determine their back azimuth, and may be of practical benefit where resources are limited and large sensor networks or arrays are not feasible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".