Field Measurements of Land Seismic Ambient Noise and Their Implications for the Effectiveness of Geophone Arrays
Bibliographic record
Abstract
Summary Historically, arrays have been used to attenuate ambient noise under the assumption that the level of attenuation is directly proportional to the square root of the number of sensors in the array. Given the availability of high channel-count point-receiver systems and the cost associated with laying out large arrays this assumption of ‘spatial randomness’ requires further analysis. Using measurements of ambient noise made at various sites in Perth, Australia with closely spaced geophones we show that ambient noise is strongly correlated over distances of up to 10 m. This correlation reduces the signal-to-ambient-noise performance of an array considerably. The correlation coefficient can be modelled using an exponential function and the correlation-distance used to determine the efficient geophone spacing. The optimum geophone spacing on days with a low wind speed (< 10 km/h, observed on 27% of days in the area) is 15 m. For days with a very high wind speed (> 80 km/h) the optimum spacing is 2.5 m, although this wind speed is very uncommon, occurring on average less than once each year. For more than 90% of days the wind speed is such that the optimum geophone spacing required for ambient noise suppression is 7.5 m.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".