Integrating Magnetotelluric and Seismic Images of Silicic Magma Systems: A Case Study From the Laguna del Maule Volcanic Field, Central Chile
Bibliographic record
Abstract
Abstract Imaging silicic systems using geophysics is challenging because many interrelated factors (e.g., temperature, melt fraction, melt composition, geometry) can contribute to the measured geophysical anomaly. Joint interpretation of models from multiple geophysical methods can better constrain interpretations of the subsurface structure. Previously published resistivity and shear wave velocity (Vs) models, derived separately from magnetotelluric (MT) and surface wave seismic data, respectively, have been used to model the restless Laguna del Maule Volcanic Field, central Chile. The Vs model contains a 450 km 3 low‐velocity zone (LVZ) interpreted as a region with an average melt fraction of 5–6%. The resistivity model contains a conductor (C3) interpreted as a region with a melt fraction >35%. The spatial extents of the LVZ and C3 overlap, but the geometries and interpretations of these features are different. To resolve these discrepancies, this study investigates the resolution of the MT data using hypothesis testing and constrained MT inversions. It is shown that the MT data are best fit with discrete conductors embedded within the larger LVZ. The differences between the MT and seismic models reflect resolution differences between the two data sets as well as varying sensitivities to physical properties. The MT data are sensitive to smaller volumes of extractable mush that contain well‐connected crystal‐poor melt (C3). The seismic data have lower spatial resolution but image the full extent of the poorly connected crystal‐rich magma storage system. The combined images suggest that the LdMVF magma plumbing system is thermally heterogeneous with coexisting zones of warm and cold storage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".