Time Evolution of Deformation Using Time Series of Differential Interferograms: Application to La Palma Island (Canary Islands)
Bibliographic record
Abstract
Differential interferometry is a very powerful tool for detecting changes in the Earth’s crust where coherence conditions are good, but is difficult to employ in some volcanic areas due to dense vegetation. We apply two differential InSAR methods using the time series associated with the interferograms to perform a phase analysis on a data set for La Palma island (Canary Islands) from the ERS-1 and ERS-2 European Space Agency (ESA) satellites for the time period 1992 to 2000. Both methods involve choosing a master image from the database and creating a series of interferograms with respect to this image. The “Coherent Pixel Time Series” (CPTS) technique chooses pixels with good average coherence, aligns the unwrapped interferograms with a stable area and then performs an inversion to calculate the linear velocity to quantify the deformation. The Coherent Target Modeling (CTM) method calculates the temporal coherence of each pixel to identify stable targets and then determines the best velocity for each pixel by using a linear fit that maximizes the temporal coherence. Using these two methods we have been able to detect deformation on La Palma Island that has been previously undetectable by conventional InSAR methods. There is a roughly circular region on the Southern part of the island that is actively deforming at ∽ -4 to -8 mm/yr. This region is located near the Teneguia valcano, the host of the last known eruption on La Palma in 1971. A thorough investigation of the possible sources for this deformation revealed that it was most likely created by a subsurface thermal source.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".