Ongoing automated ground deformation monitoring of Domuyo - Laguna del Maule area (Argentina) using Sentinel-1 MSBAS time series: Methodology description and first observations for the period 2015–2020
Bibliographic record
Abstract
Synthetic Aperture Radar (SAR) Differential Interferometry (DInSAR) and its extensions to manage time series are nowadays well-known and mastered techniques to perform among others land-motion monitoring. We present here a fully automated monitoring service based on DInSAR and Multidimensional Small Baseline Subset (MSBAS) methods. The automated processing chain named “InSAR automated Mass processing Toolbox for multidimensional Time series” (MasTer) is demonstrated with the peculiar case of the Laguna del Maule and Domuyo volcanoes, which are known to undergo significant inflation up to several cm or tens of cm per year. The here-described monitoring service is made of three components: an InSAR command line processor, the MSBAS processor and a number of shell scripts automatizing all tasks, from data downloading to updated displacement maps and time series. Thanks to the European Commission Copernicus Service, data are freely available every 12 or 6 days along each orbit, depending on ESA acquisition plan. Linear deformation rate maps and time series of vertical and East-West ground deformation computed by the MasTer tool are updated every 1–6 days on a web page, according to the S1 data availability. Interested user can request additional time series at coherent pixels that wouldn't be displayed on the web page and receive them by e-mail within a minute. Processing chain and preliminary observations at Domuyo and Laguna del Maule test site are presented and commented. The nearly 6 years long 2D time series of ground deformation allow highlighting recent changes in the well-known deformation at both volcanoes: Domuyo inflation stabilizes since beginning of 2019 while deformation at Laguna del Maule seems to gently accelerate since mid 2018. New deformation features are also identified, such as a 2 km wide 3 cm/yr circular subsidence at Laguna del Maule as well as a 2 cm/yr downward and 1 cm/yr eastward movement located to the East of Laguna Fea.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".