Formation of the El Laco magmatic magnetite deposits by Fe-Si melt immiscibility and bubbly suspension flow along volcano tectonic faults
Bibliographic record
Abstract
The origin of Kiruna-type magnetite-apatite deposits, which are thought to form by magmatic and/or hydrothermal processes, has recently come under renewed scrutiny. Geological and geochemical studies of volcanic-hosted magnetite deposits that include magnetite lava flows and ash layers at El Laco, a volcano in the Central Volcanic Zone, northern Chile, suggest a formation by eruptive emplacement of an iron oxide-rich melt. The generation of such exotic high density, low viscosity melts by dissociation from an andesitic host magma contaminated by shallow crustal sediments has only recently been shown experimentally. The dynamics of volcanic emplacement have remained enigmatic because the high density of iron-rich melts seems to negate their eruption potential. Yet, observations of ubiquitous vesiculation, degassing structures, and steam-heated alteration provide important clues that volatiles had a pivotal role in the volcanic emplacement. Here, we posit a scenario in which an iron-rich immiscible liquid gravitationally separates from its andesitic parent magma in a shallow magma reservoir and subsequently rises as a bubbly suspension along volcano-tectonic faults extending to the flanks of the edifice. We test this hypothesis through numerical models that capture both the deformation of the volcanic edifice as well as the melt transport within. Preliminary results indicate that separation of a low-viscosity, iron- and volatile-rich melt from a silicic magma within a reasonable time is possible only if an interconnected melt drainage networks forms at the granular scale. Results further suggest that magma reservoir deflation and/or minor local extension combined with the topographic load of the edifice may explain normal faults connecting the magma reservoir with magnetite flow locations on the volcano flanks. Finally, our models show that hydrostatically driven flow of iron-rich melts into these faults at depth may trigger volatile exsolution and bubble expansion to provide sufficient driving force for an eruptive emplacement. Although the case for such magmatic ore formation is perhaps strongest at El Laco, evidence from other localities suggests that similar processes have been at work. The new insights derived from our models may, therefore, apply more generally to Kiruna-type deposits elsewhere.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".