Two-dimensional determinant inversion of marine magnetotelluric data and a field example from the Gulf of California, Mexico
Bibliographic record
Abstract
ABSTRACT Two-dimensional marine magnetotelluric (MT) observations are useful for offshore geologic studies, such as natural resource exploration, fault mapping, fluid estimation at subduction zones, and the delineation of the lithosphere-asthenosphere boundary beneath the seafloor. Earth structures are often assumed to be two dimensional, which allows MT data to be decomposed into a transverse electric (TE) mode and a transverse magnetic (TM) mode. The 2D assumption can effectively reduce acquisition and computational costs. However, offline 3D effects and other problems such as lack/failure of the compass on instruments are often encountered, making it difficult to decompose data into the TE and TM modes. In these cases, 2D inversion may be misleading or may not provide an acceptable misfit to the marine MT observations. Thus, we have developed a 2D determinant inversion to the marine MT method to mitigate these difficulties, implemented in the MARE2DEM code, and we tested its utility using synthetic examples and a field example. In the synthetic examples, the determinant inversion demonstrates an ability to overcome 3D effects caused by 3D anomalies and bathymetry. With confidence from the synthetic tests, we interpreted real data acquired in the Gulf of California, Mexico, where not only is the bathymetry 3D in nature, but the external compasses failed to record the orientation. The field data can not only be fit to a reasonable misfit with a determinant inversion, but the resolved conductive zones also have a good correlation with known faults. A comparison between the resistivity model from the field data and a seismic reflection section shows that a previously interpreted fault, the Wagner Fault, should be shifted 5 km toward the southwest and made slightly steeper. Thus, the implementation of the determinant inversion may provide a new approach for using problematic 2D data.
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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 | 0.000 | 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".