Review and perspectives: Understanding natural‐hazards‐generated ionospheric perturbations using GPS measurements and coupled modeling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Natural hazards including earthquakes, volcanic eruptions, and tsunamis have been significant threats to humans throughout recorded history. Global navigation satellite systems (GNSS; including the Global Positioning System (GPS)) receivers have become primary sensors to measure signatures associated with natural hazards. These signatures typically include GPS‐derived seismic deformation measurements, coseismic vertical displacements, and real‐time GPS‐derived ocean buoy positioning estimates. Another way to use GPS observables is to compute the ionospheric total electron content (TEC) to measure, model, and monitor postseismic ionospheric disturbances caused by, e.g., earthquakes, volcanic eruptions, and tsunamis. In this paper, we review research progress at the Jet Propulsion Laboratory and elsewhere using examples of ground‐based and spaceborne observation of natural hazards that generated TEC perturbations. We present results for state‐of‐the‐art imaging using ground‐based and spaceborne ionospheric measurements and coupled atmosphere‐ionosphere modeling of ionospheric TEC perturbations. We also report advancements and chart future directions in modeling and inversion techniques to estimate tsunami wave heights and ground surface displacements using TEC measurements and error estimates. Our initial retrievals strongly suggest that both ground‐based and spaceborne GPS remote sensing techniques could play a critical role in detection and imaging of the upper atmosphere signatures of natural hazards including earthquakes and tsunamis. We found that combining ground‐based and spaceborne measurements may be crucial in estimating critical geophysical parameters such as tsunami wave heights and ground surface displacements using TEC observations. The GNSS‐based remote sensing of natural‐hazard‐induced ionospheric disturbances could be applied to and used in operational tsunami and earthquake early warning systems.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it