Unified geophysical and geological 3D Earth models
Bibliographic record
Abstract
Three-dimensional geological Earth models typically comprise wireframe surfaces of connected triangles that represent geological contacts. In contrast, Earth models used by most current 3D geophysical numerical modeling and inversion methods are built on rectilinear meshes. This is because the mathematics for computing data responses are simpler on rectilinear meshes. In such a model, the relevant physical properties are uniform within each brick-like cell but possibly different from one cell to the next, producing a pixellated representation of the Earth. In principle, arbitrary spatial variations can be represented if a sufficiently fine discretization is used. However, no matter how fine the discretization of the rectilinear mesh, such a mesh is always incompatible with geological models comprising wireframe surfaces. Also, because the computational resources required by 3D numerical modeling and inversion methods increase dramatically as the discretization of a model is refined, it is never really possible to achieve as fine a discretization as one would like. This exacerbates the mismatch between models that comprise wireframe surfaces and those built on rectilinear meshes. To address this incompatibility, we are using unstructured tetrahedral meshes to specify 3D geophysical Earth models. We hope that working with unstructured meshes will facilitate the construction of common Earth models consistent with both the geological and geophysical data available.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".