Surface geometry inversion of time-domain EM data
Bibliographic record
Abstract
Standard minimum-structure inversions normally recover smooth models which do not have distinct boundaries between different geological units. While this works well for geological scenarios with smoothly varying mineralization and hence physical properties, it struggles to recover thin structures with a large physical property contrast with their hosts. We have implemented a surface geometry inversion for time-domain electromagnetic data. This method parameterizes the Earth model in terms of wireframe surfaces, and the inversion solves for the coordinates of the facet vertices in these surfaces while keeping the conductivities of the different units fixed. To compute the electromagnetic data, we discretize the volumes between the wireframe surfaces with unstructured tetrahedral grids and use a finite-element solver. We use a genetic algorithm to minimize the data misfit. We demonstrate the capabilities of this surface geometry inversion here with basic, preliminary examples. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 8:30 AM Presentation Time: 11:25 AM Location: 351D Presentation Type: Oral
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".