Review and perspectives: Understanding natural‐hazards‐generated ionospheric perturbations using GPS measurements and coupled modeling
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".