Kalman Filter Algorithm for the joint Processing of GNSS PPP and Accelerometer Data, EEW parameters from the unbiased displacement time-series
Bibliographic record
Abstract
The filter owes its name to R. E. Kalman at the Institute for Advanced Studies in Baltimore (Kalman, 1960), developed well before the start of the digital age. It has since found numerous applications in control, signal estimation and general filtering problems. Although similar proposals have been made before (c.f. Mayhew, 1999) in a different context, the proposal to apply a Kalman filter to the problem of combining observations from the Global Navigation Satellite Systems (GNSS) with data from a three axis accelerometer was initially proposed by Smyth and Wu, 2007. The general idea of the Kalman filter is to predict a system’s behaviour from incomplete observations. The problem here is to predict high resolution displacement and velocity from acceleration and Precise Point Position (PPP) data which are available at different times and different rates from the accelerometer and corrected GNSS data respectively. The idea to apply this to real-time seismology and earthquake early warning (EEW) goes back to Bock, Melgar, and Crowell, 2011 and has been validated in several studies (Melgar et al., 2013; Li, 2015; Niu and Xu, 2014) where various data-sets from large earthquakes were processed off-line, after the event.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".