Finding realistic dike models from interferometric synthetic aperture radar data: The February 2000 eruption at Piton de la Fournaise
Bibliographic record
Abstract
Dike intrusions often cause complex ground displacements that are not sufficiently explained by simple analytical models. We develop a method to find complex and realistic dike geometries and overpressures from interferometric synthetic aperture radar (InSAR) data. This method is based on a combination of a boundary element method with realistic topography and a neighborhood algorithm inversion. Dike model geometry is roughly a quadrangle with its top reaching the ground. The inversion has two stages: search and appraisal. The appraisal stage involves calculations of model marginal probability density functions using misfit values calculated during the search stage. The misfit function takes into account the variance and correlation of data noise. Synthetic tests show that a model is successfully retrieved within predicted narrow confidence intervals. We apply the method on InSAR data of the February 2000 flank eruption at Piton de la Fournaise and get a trapezoid dike dipping seaward (61.0°–67.3°) with its bottom passing 800–1000 m beneath the summit. A model with a basal slip plane does not better explain observed asymmetric displacements, and thus this asymmetry is solely attributed to the dipping dike. The dike lies above a narrow band of preeruption seismicity, suggesting that lateral magma propagation occurred. Neglecting topography results in poor modeling at depth and in overestimations of overpressure (or opening), height (both about 30%), and volume (about 20%).
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".