Using GNSS Phase Observation Residuals and Wavelet Analysis to Detect Earthquakes
Bibliographic record
Abstract
Abstract Global Navigation Satellite Systems (GNSSs) are one of the techniques that can be used for the deformation monitoring caused by earthquakes. GNSSs enable the monitoring of specific areas affected by earthquakes in real-time and post-processing modes. This paper provides a novel method for the short-term displacement detection based on the phase residuals of GNSS observations. The study examines a natural earthquake from October 26, 2016, in Italy with Mw 6.1. In the proposed methodology, the residuals of the GNSS phase observations are derived using Precise Point Positioning. Then, residuals are transformed to the frequency domain using a wavelet transform, and the earthquake moment is identified using the anomalies detected in the scalogram, which do not appear in stable conditions. The described methodology allows for detecting the starting and ending moments of the earthquake. The methods described detected that the moment of the earthquake is visible in the residues 8 seconds after the seismic catalog time. The conducted experiments show that the use of signal analysis tools allows it to properly detect the ground vibrations in the residuals of GNSS phase observations and thus confirms the registration of ground vibrations in satellite observation data recorded by a GNSS receiver.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".