The coherency of ambient seismic noise recorded during land surveys and the resulting implications for the effectiveness of geophone arrays
Bibliographic record
Abstract
ABSTRACT The use of analog sensor arrays is often assumed to provide signal-to-ambient-noise improvements proportional to the square root of the number of sensors being summed. We determined via numerical modeling and field experiments that the improvements sought were significantly hindered once the ambient noise exhibited coherence over the array being summed. As a first step, a numerical model was developed to explore the optimal sensor spacing based on the average correlation coefficient between sensors. Field experiments were then carried out to measure ambient noise using closely spaced geophones at several sites in Perth, Western Australia. We show that the measured noise at six test sites was strongly coherent over distances of up to 10 m, with the level of coherency being inversely proportional to frequency. The resulting optimum geophone spacing under our field conditions was determined to be 7.5 m. These results offered further encouragement to reassess our use of analog arrays and to consider recording the output from individual sensors. In addition to the benefits from postacquisition noise suppression, this approach will enable increased trace density without degradation of the signal caused by strong coherent noise sources over acquisition spreads.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".