Constrained geophysical inversion on unstructured meshes
Bibliographic record
Abstract
To be reliable, Earth models used for mineral exploration, or other subsurface investigations, should be consistent with all available geological and geophysical information. Geophysical inversion provides the means to integrate geological data such as lithology and structure, physical property data such as measurements taken on rock samples, and geophysical survey data. Inversion is a computational process that recovers models of the subsurface that could have given rise to measured data from a geophysical survey, for example, seismic, gravity, magnetic, electrical and electromagnetic measurements, while maintaining consistency with the geological knowledge available. We are performing geophysical inversion on unstructured meshes because they provide the flexibility required to efficiently incorporate complicated geological information. There are some significant challenges involved in working with unstructured meshes. These include the generation and storage of unstructured meshes and developing appropriate matrix operators for numerical forward modelling and inversion. A major challenge is developing appropriate smoothness regularization and gradient operators on unstructured triangular and tetrahedral meshes. We apply our methods to a 3D example inspired by a real mineral exploration scenario.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".