Multidimensional time-series analysis of ground deformation from multiple InSAR data sets applied to Virunga Volcanic Province
Bibliographic record
Abstract
A novel, multidimensional small baseline subset (MSBAS) methodology is presented for integration of multiple interferometric synthetic aperture radar (InSAR) data sets for computation of 2- or 3-D time-series of deformation. The proposed approach allows the combination of all possible air-borne and space-borne SAR data acquired with different acquisition parameters, temporal and spatial sampling and resolution, wave-band and polarization. The produced time-series have improved temporal resolution and can be enhanced by applying either regularization or temporal filtering to remove high-frequency noise. We apply this methodology to map 2003–2010 ground deformation of the Virunga Volcanic Province (VVP), North Kivu, Democratic Republic of Congo. The horizontal and vertical time-series of ground displacement clearly identify lava compaction areas, long-term deformation of Mt Nyamuragira and 2004, 2006 and 2010 pre- and coeruptive deformation. Providing that enough SAR data is available, the method opens new opportunities for detecting ground motion in the VVP and elsewhere.
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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) | 0.000 | 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 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".