Real-time earthquake hazard assessment based on high-rate GNSS PPPAR
Bibliographic record
Abstract
To provide hazard assessment in rapid or real-time mode, accelerations due to seismic waves have traditionally been recorded by seismometers. Another approach, based on the Global Navigation Satellite System (GNSS), known as GNSS seismology, has become increasingly accurate and reliable. In the past decade, significant improvements have been made in high-rate GNSS using precise point positioning and its ambiguity resolution (PPPAR). To reach cm-level accuracy, however, PPPAR requires specific products, including satellite orbit/clock corrections and phase/code biases generated by large GNSS networks. Therefore, the use of PPPAR in real-time seismology applications has been inhibited by the limitations in product accessibility, latency, and accuracy. To minimize the implementation barrier for ordinary global users, the Centre National D’Etudes Spatiales (CNES) in France has launched a public PPPAR correction service via real-time internet streams. Broadcasting via the real-time service (RTS) of the international GNSS service (IGS), the correction stream is freely provided. Therefore, in our work, a new approach using PPPAR assisted with the CNES product to process high-rate in-field GNSS measurements is proposed for real-time earthquake hazard assessment. A case study is presented for the Ridgecrest, California earthquake sequence in 2019. The general performance of our approach is evaluated by assessing the quality of the resulting waveforms against publicly available post-processing GNSS results from a previous study by Melgar et al. (2019), Seismol. Res. Lett. XX, 1–9, doi: 10.1785/ 0220190223. Even though the derived real-time displacements are noisy due to the accuracy limitation of the CNES product, the results show a cm-level agreement with the provided post-processed control values in terms of root-mean-square (RMS) values in time and frequency domain, as well as seismic features of peak-ground-displacement (PGD) and peak-ground-velocity (PGV). Overall, we have shown that high-rate GNSS processing based on PPPAR via a freely accessible service like CNES is a reliable approach that can be utilized for real-time seismic hazard assessment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".