Using Borehole Records to Estimate Magnitude for Earthquake and Tsunami Early-Warning Systems
Bibliographic record
Abstract
This paper presents a new application of the ground‐motion prediction equations (GMPEs) to estimate the event magnitude for earthquake and tsunami early warning systems. This technique incorporates borehole strong‐motion records along with surface recordings provided by Kiban Kyoshin network (KiK‐net) stations. We analyzed strong ground motion data from earthquakes with moment magnitude ( M ) ranging from 5.0 to 8.1 recorded by KiK‐net stations provided by Japan’s National Research Institute for Earth Science and Disaster Prevention (NIED) over the interval of 1998 to 2010. We used 2160 strong ground motion accelerograms with peak ground acceleration (PGA) larger than 10 cm/s2 recorded by borehole seismographs, and 890 waveforms with PGA larger than 80 cm/s2 recorded by surface seismographs to derive GMPEs for PGA and peak ground velocity (PGV) in Japan. These GMPEs are used as the basis for regional magnitude determination. Predicted magnitudes from PGA values (MPGA) and predicted magnitudes from PGV values (MPGV) were defined separately for borehole and surface recordings. MPGA and MPGV strongly correlate with the moment magnitude of the event, provided that at least 20 records for each event are available. The results show that MPGV from borehole data has the smallest standard deviation among the estimated magnitudes and provides an accurate early assessment of earthquake magnitude. We demonstrate that incorporation of borehole strong ground motion records immediately available after the occurrence of large earthquakes significantly increases the accuracy of earthquake magnitude estimation and improves earthquake and tsunami early warning systems performance.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| 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".