From Relative to Absolute Teleseismic Travel Times: The Absolute Arrival‐Time Recovery Method (AARM)
Bibliographic record
Abstract
Dense, short‐term deployments of seismograph networks are frequently used to study upper‐mantle structure. However, recordings of variably emergent teleseismic waveforms are often of lower signal‐to‐noise ratio (SNR) than those recorded at permanent observatory sites. Therefore, waveform coherency across a network is frequently utilized to calculate relative arrival times between recorded traces, but these measurements cannot easily be combined or reported directly to global absolute arrival‐time databases. These datasets are thus a valuable but untapped resource with which to fill spatial gaps in global absolute‐wavespeed tomographic models. We developed an absolute arrival‐time recovery method (AARM) to retrieve absolute time picks from relative‐arrival‐time datasets, working synchronously with filtered and unfiltered data. We also include a relative estimate of uncertainty for potential use in data weighting during subsequent tomographic inversion. Filtered waveforms are first aligned via multichannel cross correlation. These time shifts are applied to unfiltered waveforms to generate a phase‐weighted stack. Cross correlation with the primary stack or the SNR of each trace is used to weight a second‐higher SNR stack. The first arrival on the final stack is picked manually to recover absolute arrival times for the aligned waveforms. We test AARM on a recently published dataset from southeast Canada ( ∼10,000∼10,000 picks). When compared with the available equivalent earthquake–station pairs on the International Seismological Centre (ISC) database, ∼83%∼83% of AARM picks agree to within ±0.5 s±0.5 s . Tests using synthetic P‐wave data indicate that AARM produces absolute arrival‐time picks to accuracies of better than 0.25 s, akin to uncertainties in ISC bulletins.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".