Modeling long‐term volcanic hazards through Bayesian inference: An example from the Tohoku volcanic arc, Japan
Bibliographic record
Abstract
The need to quantitatively estimate future locations of volcanoes in the long‐term is of increasing importance, partly as a result of the requirement of constructing certain types of installations in regions of low geologic risk. The complex geological factors and natural processes controlling the locations of volcanoes make it problematic to estimate future patterns deterministically. Instead, the probabilistic approach can be developed with quite high levels of confidence; however, for regions with few or no volcanoes, there is a need to include additional geological and geophysical data that may indicate the likelihood of future volcanism. We achieve this using Bayesian inference in the Tohoku volcanic arc, Japan, in order to combine one or more sets of geophysical information to a priori assumptions of volcano spatiotemporal distributions yielding modified a posteriori probabilities. The basic a priori assumption is that new volcanoes will not form far from existing ones and that such a distribution ranges from Gaussian (not so conservative) to Cauchy (conservative). Seismic tomographs are used as an indirect clue, and from this geophysical data a likelihood function is generated in the Bayesian context that updates or fine tunes the initial Gaussian or Cauchy kernels to better reflect the distribution of future volcanism. These models are evaluated using pre‐100 ka volcanic events to forecast locations of subsequent events that actually formed from 100 kyr ago to present. Probabilities in Tohoku region range from 10−10/yr between clusters and up to 9.8 × 10−6/yr near the centers of clusters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".