Why not drill into magma to understand the “dynamics and timescales in magmatic reservoirs”?
Bibliographic record
Abstract
How can we not afford to scientifically probe magma? Fifteen years of accidental drilling encounters with magma have shown that it can be done safely with recovery of magmatic and partial melt samples quenched in situ. More could be gained if preceded by thorough scientific preparation and followed by long-term monitoring. Through the panoply of instruments now available, we can measure temperature, pressure, strain, heat and mass transport and changes over time. In 2009, the Iceland Deep Drilling Program well #1 reached rhyolitic magma at 2100 m depth under Krafla Caldera. The project was exemplary in sharing provocative results, but only hints at what is possible. Equilibrium temperatures were estimated by traditional petrologic techniques to be 850 – 1100 C. Pressure estimates range from 40 – 90 MPa with both extremes seemingly problematic, because for the first time we know the depth of a magma body to 4 significant figures. The lowest value is below lithostatic and the highest could be inherited from deeper levels. Now it appears that the lower pressure is what magma “feels”. But without drilling, would traditional estimates be good enough? Magma is somewhere between 1500 – 4000 m depth and with temperature corresponding to some type of magma? Actually, we would not even know that shallow magma is there but now in hindsight we see it geophysically. Ground-truth testing is how methodologies are improved. Our situation is like speculating about the nature of the Moon without sampling it. The cost of probing Earth’s magma is high and the probability of success uncertain, but far less so on either count than for extraterrestrial exploration. On Earth we are more restrained by self-imposed limits than by our technical capabilities. Besides understanding the differentiation of our planet, we have two compelling reasons for bold exploration: 1) We need the baseload, magma resource with its far higher temperature, energy density, and more extensive thermal fracturing than conventional geothermal; 2) We need to raise the level of reliability of eruption forecasts by testing our magma-dynamic models directly, thereby saving countless lives. As with other endeavors that are expensive for a single country to undertake but that benefit all humankind, a way forward is through an international infrastructure, where teams of scientists can conduct experiments with magma and superhot fluids. This is analogous to particle accelerators and the complement to outer space travel: inner space. The Krafla Magma Testbed is a much-needed step and an opportunity for all planetary, magma, volcano, and hydrothermal scientists to test their methods and ideas. KMT will drill a doublet of wells to magma for long-term monitoring and experimentation, respectively. The project, now organized as a legal entity within the Iceland Geothermal Research Cluster (GEORG), in partnership with the National Power Company of Iceland (Landsvirkjun), Iceland Energy GeoSurvey (ISOR), and a multinational team of scientists and engineers, under the aegis of the International Continental Scientific Drilling Program (ICDP), is ready. Magma could have been intentionally explored before. It is time to ask, “Why not now?”
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.018 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".