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Real-time earthquake hazard assessment based on high-rate GNSS PPPAR

2020· article· en· W3090760027 on OpenAlexaff
Yang Jiang, Yang Gao, Michael G. Sideris

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGNSS applicationsComputer scienceReal-time computingSatellite systemGlobal Positioning SystemAmbiguity resolutionGNSS augmentationPrecise Point PositioningSeismometerTelecommunicationsGeologySeismology

Abstract

fetched live from OpenAlex

<p>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.</p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.410
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.012
GPT teacher head0.221
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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