Multidimensional Small Baseline Subset (MSBAS) Software for Constrained and Unconstrained Deformation Analysis of Partially Coherent DInSAR and Speckle Offset Data
Bibliographic record
Abstract
Differential Interferometric Synthetic Aperture Radar (DInSAR) and speckle offset data are used to measure ground deformation. Computing deformation time series from these data can be challenging due to varying radar line-of-sight acquisition geometry, rapid coherence loss, and the large datasets involved requiring computationally intensive processing. The Multidimensional Small Baseline Subset (MSBAS) Software is designed to compute deformation time series from these data, and its parallelized version is optimized to handle large datasets from modern SAR satellites. The software can produce 1D, 2D, and 3D Surface-Parallel and Aspect-Parallel Flow constrained and 3D unconstrained or 4D deformation time series. It runs on workstations and clusters utilizing OpenMP and MPI/OpenMP. This study presents novel results demonstrating MSBAS capabilities, such as landslides in Northwest Territories, Argentina, and Colombia, as well as tectonic deformation and subsidence in Turkey and Yemen. A 4D deformation time series of glacier flow and surge at the Malaspina Glacier in Alaska is showcased. Equations for solving multidimensional problems are included along with processing parameters and supplemental data used to create constraints. The manuscript serves as a comprehensive manual for the MSBAS Software Version 10. The source code is available.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".