Depth‐Scanning Algorithm: Accurate, Automatic, and Efficient Determination of Focal Depths for Local and Regional Earthquakes
Bibliographic record
Abstract
Abstract Precise source depths are critical to the understanding and interpretation of many seismological and tectonic processes. However, conventional earthquake location methods based on the arrival times of direct P and S phases have far less constraint on focal depths than on epicenters. Therefore, developing a method that can systematically and efficiently estimate focal depths with high accuracy, especially for the vast number of small earthquakes that can only be observed at local and regional distances, would make significant contributions to the geoscience research community. In this study, we develop a new method, named “Depth‐Scanning Algorithm,” to efficiently identify depth phases at local and regional distances. We first construct template waveforms of possible depth phases by applying various phase shifts to the original P and S waveforms to mimic the effect of reflection(s). We then systematically scan waveforms after the P and S phases for segments that match the depth‐phase templates. The arrival times of those segments are compared to the theoretical arrival times of depth phases predicted with an assumed velocity model and focal depth. We repeat the above process for a range of assumed focal depths, and the one most consistent with the theoretical prediction is deemed the final solution. Synthetic tests and applications to real data demonstrate the merits of our method compared to conventional location methods.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".