High Spatio-Temporal Resolution Deformation Time Series With the Fusion of InSAR and GNSS Data Using Spatio-Temporal Random Effect Model
Bibliographic record
Abstract
High spatio-temporal resolution deformation series can be used to improve the understanding of deformation mechanism, thereby contributing to prevention and control of geological disasters such as mine subsidence, landslide, and earthquake. Among ground deformation monitoring technologies, global navigation satellite system has high temporal resolution but low spatial resolution, and interferometric synthetic aperture radar (InSAR) has high spatial resolution but low temporal resolution. Fusing these two data may generate high spatio-temporal resolution deformation series. Existing fusion methods usually use the bi-direction interpolation, which does not consider the spatio-temporal cross correlation and is computationally extensive. We propose a dynamic filtering fusion model based on the spatio-temporal random effect (a spatio-temporal Kalman filter) model. Experiments with simulated data and real data from the Los Angeles area are conducted to validate this method. Simulated experimental results are compared with truth data and the Los Angeles experiment data results are verified using the leave-one InSAR image-out validation method. The RMS results for them are around 13.8 and 5 mm, respectively, indicating that the proposed method can achieve high accuracy and high spatial-temporal resolution deformation time series.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".