Signal‐to‐noise ratios of teleseismic receiver functions and effectiveness of stacking for their enhancement
Bibliographic record
Abstract
We present a method for the measurement of spatially variable signal‐to‐noise (S/N) ratios in multichannel teleseismic receiver function (RF) images. The S/N ratio is defined as a measure of coherency of the final image, and the approach is applicable to any RF imaging technique that employs mapping of the records into depth followed by their summation as the final signal enhancement step. In such methods, all of the converted phases become horizontally aligned in the depth domain, and their coherent (signal) and incoherent (noise) components can be estimated by using stacking statistics. For 10 locations along two subarrays of the Continental Dynamics of Rocky Mountains Project (CD‐ROM) teleseismic array, after a limited RF editing, we apply our method to the image resulting from three‐dimensional (3‐D) prestack RF depth migration. The resulting amplitude S/N values to vary from 0.1 to 0.3 in the individual RFs and from 1 to 5 in the final image, with significant spatial variability. These moderate S/N values argue in favor of sampling redundancy achieved in array recording and multichannel RF processing. In order to further reduce noise in RF images, denser and longer deployments or additional signal enhancement techniques are advisable. Because of its ability to provide direct assessment of image quality in three dimensions, including its dependence on the frequency band and data coverage, our method can also be potentially used for real‐time array tuning and image focusing required for moving arrays proposed for the USArray program.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".