Interpretation of volcanic surface deformation using a 3D multi-source approach
Bibliographic record
Abstract
Volcano geodetic observation is a valuable tool to infer location, strength and geometry of magmatic plumbing systems. The availability of high precision and spatial resolution, spanning decades, deformation data from satellite radar observation and Global Navigation Satellite Systems (GNSS) can give us important information for detecting and characterizing their temporal variations as well as other possible geodynamic sources acting in the volcanic area. For this objective inversion techniques are necessary which help us to obtain the maximum of information from these new datasets. We present a new, original methodology to carry out a multi-source inversion of ground deformation data to better understand the subsurface causative processes (Camacho et al., 2020). The methodology uses a nonlinear approach which permits the determination of location, size and three-dimensional configuration, without any a priori assumption as to the number, nature or shape of the potential sources. The proposed method identifies a combination of pressure bodies and different types of dislocation sources (dip-slip, strike-slip and tensile) representing magmatic sources and other processes such as earthquakes, landslides or groundwater-induced subsidence through the aggregation of elemental cells. This approach carries out a simultaneous inversion of the deformation components and/or line-of-sight (LOS) data; and a simultaneous determination of diverse structures such as pressure bodies or dislocation sources, representing local and regional effects. Both things are done in a fully 3D context and without any initial hypothesis about the number, geometry or types of the causative sources is necessary. We show results from the application of this new methodology to synthetic and real test cases (e.g., Mt. Etna). This research has been primarily supported by the Spanish Ministerio de Ciencia, Innovación and Universidades research project DEEP-MAPS (RTI2018-093874-B-I00) and is part of the CSIC-PTIs TELEDETEC and POLARCSIC activities. References Camacho, A.G., Fernández, J., Samsonov, S.V., Tiampo K.F., Palano, M., 2020. Multisource 3D modelling of elastic volcanic ground deformations. Earth and Planetary Science Letters, 547C, 116445. https://doi.org/10.1016/j.epsl.2020.116445.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".