Combining double-difference relocation with regional depth-phase modelling to improve hypocentre accuracy
Bibliographic record
Abstract
Precise and accurate earthquake hypocentres are critical for various fields, such as the study of tectonic process and seismic-hazard assessment. Double-difference relocation methods are widely used and can dramatically improve the precision of event relative locations. In areas of sparse seismic network coverage, however, a significant trade-off exists between focal depth, epicentral location and the origin time. Regional depth-phase modelling (RDPM) is suitable for sparse networks and can provide focal-depth information that is relatively insensitive to uncertainties in epicentral location and independent of errors in the origin time. Here, we propose a hybrid method in which focal depth is determined using RDPM and then treated as a fixed parameter in subsequent double-difference calculations, thus reducing the size of the system of equations and increasing the precision of the hypocentral solutions. Based on examples using small earthquakes from eastern Canada and southwestern USA, we show that the application of this technique yields solutions that appear to be more robust and accurate than those obtained by standard double-difference relocation method alone.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".