Percolating magmas in three dimensions
Bibliographic record
Abstract
Abstract. The classical models of volcanic eruptions assume that they originate as a consequence of critical stresses or critical strain rates being exceeded in the magma followed by catastrophic fragmentation. In a recent paper (Gaonac'h et al., 2003) we proposed an additional mechanism based on the properties of complex networks of overlapping bubbles; that extreme multibubble coalescence could lead to catastrophic changes in the magma rheology at a critical vesicularity. This is possible because at a critical vesicularity Pc (the percolation threshold), even in the absence of external stresses the magma fragments. By considering 2-D percolation with the (observed) extreme power law bubble distributions, we showed numerically that P2c had the apparently realistic value ≈0.7. The properties of percolating systems are, however, significantly different in 2-D and 3-D. In this paper, we discuss various new features relevant to 3-D percolation and compare the model predictions with empirical data on explosive volcanism. The most important points are a) bubbles and magma have different 3-D critical percolation points; we show numerically that with power law bubble distributions that the important magma percolation threshold P3c,m has the high value ≈0.97±0.01, b) a generic result of 3-D percolation is that the resulting primary fragments will have power law distributions with exponent B3f≈1.186±0.002, near the empirical value (for pumice) ≈1.1±0.1; c) we review the relevant percolation literature and point out that the elastic properties may have lower – possibly more realistic – critical vesicularities relevant to magmas; d) we explore the implications of long range correlations (power law bubble distributions) and discuss this in combination with bubble anisotropy; e) we propose a new kind of intermediate "elliptical" dimensional percolation involving differentially elongated bubbles and show that it can lead to somewhat lower critical thresholds. These percolation mechanisms for catastrophically weakening magma would presumably operate in conjunction with the classical critical stress and critical strain mechanisms. We conclude that percolation theory provides an attractive theoretical framework for understanding highly vesicular magma.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".