Modeled temperatures and fluid source distributions for the <scp>M</scp>exican subduction zone: Effects of hydrothermal circulation and implications for plate boundary seismic processes
Bibliographic record
Abstract
Abstract In subduction zones, spatial variations in pore fluid pressure are hypothesized to control the sliding behavior of the plate boundary fault. The pressure‐temperature paths for subducting material control the distributions of dehydration reactions, a primary control on the pore fluid pressure distribution. Thus, constraining subduction zone temperatures are required to understand the seismic processes along the plate interface. We present thermal models for three margin‐perpendicular transects in the Mexican subduction zone. We examine the potential thermal effects of vigorous fluid circulation in a high‐permeability aquifer within the basaltic basement of the oceanic crust and compare the results with models that invoke extremely high pore fluid pressures to reduce frictional heating along the megathrust. We combine thermal model results with petrological models to determine the spatial distribution of fluid release from the subducting slab and compare dewatering locations with the locations of seismicity, nonvolcanic tremor, slow‐slip events, and low‐frequency earthquakes. Simulations including hydrothermal circulation are most consistent with surface heat flux measurements. Hydrothermal circulation has a maximum cooling effect of 180°C. Hydrothermally cooled crust carries water deeper into the subduction zone; fluid release distributions in these models are most consistent with existing geophysical data. Our models predict focused fluid release, which could generate overpressures, coincident with an observed ultraslow layer (USL) and a region of nonvolcanic tremor. Landward of USLs, a downdip decrease in fluid source magnitude could result in the dissipation in overpressure in the oceanic crust without requiring a downdip increase in fault zone permeability, as posited in previous studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".