3D inversion of DC/IP data using adaptive OcTree meshes
Bibliographic record
Abstract
Data acquired from a direct current (DC) and induced polarization (IP) survey can be used to recover the conductivity and chargeability structures of the subsurface of the earth. In order to maximize the value of such a survey, the data should be inverted in 3D. As surveys get larger and targets get more complex, the discretization applied in regular rectilinear meshes can become cumbersome, resulting in prohibitively large numbers of cells. This problem is exacerbated in the presence of severe topography, or in cases of irregular survey geometry. Applying an adaptive OcTree mesh structure, it is possible to obtain fine resolution cells in regions of high variability without adding unnecessarily small cells where they are not required. This results in a vastly decreased number of cells, without penalizing the potential for high resolution recovered models. We develop the DC/IP inverse algorithm on the Oc-Tree mesh, and apply it to a field example from South America.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".