Modelling Gravity changes and crustal deformation in active volcanic areas
Bibliographic record
Abstract
Elastic half-space models are widely used to interpret displacements and gravity changes in active volcanic areas. Those models usually compute the displacement response to dilatational sources that simulate a change in pressure of the magma chamber. However, elastic-gravitational model allows one to compute gravity, deformation and potential changes due to pressurized cavities and intruded masses together. First, we interpret deformation and gravity change data in Long Valley caldera, California, by using both a classical elastic and an elastic-gravitational model. Our results show that intruded mass can not be neglected for interpretation of gravity changes while displacements are mainly caused by pressurization. Therefore, the intrusion mass together with the associated pressurization produces distinctive changes in gravity that could be used to interpret gravity changes without ground deformation or viceversa depending on what is the source playing the main role in modelling. Second, we model the source of inflation at Long Valley caldera using a Genetic Algorithm inversion technique and microgravity data (1982-1998). The results of the performed inversions fit gravity anomaly centered under the resurgent dome. The two source inversion suggest that the gravity change could be caused by a more spatially distributed source under the resurgent dome.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".