Comparison of Manual and Automated Ground Motion Processing for Small‐to‐Moderate‐Magnitude Earthquakes in Japan
Bibliographic record
Abstract
Numerous time series for small‐to‐moderate‐magnitude (SMM) earthquakes have been recorded in many regions. A uniformly‐processed ground‐motion database is essential in the development of regional ground‐motion models. An automated processing protocol is useful in developing the database for these earthquakes especially when the number of recordings is substantial. This study compares a manual and an automated ground‐motion processing methods using SMM earthquakes. The manual method was developed by the Pacific Earthquake Engineering Research Center to build the database of time series and associated ground‐motion parameters. The automated protocol was developed to build a database of pseudo‐spectral acceleration for the Kiban‐Kyoshin network recordings. Two significant differences were observed when the two methods were applied to identical acceleration time series. First, the two methods differed in the criteria for the acceptance or rejection of the time series in the database. Second, they differed in the high‐pass corner frequency used to filter noise from the acceleration time series. The influences of these differences were investigated on ground‐motion parameters to elucidate the quality of ground‐motion database for SMM earthquakes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".