A case-history tutorial describing the incorporation of geophysical, petrophysical, and geologic constraints to generate realistic geologic models of the Matheson study area, Ontario
Bibliographic record
Abstract
ABSTRACT The model used to explain potential-field data is highly dependent on the constraints applied in the modeling process. Many studies demonstrate the necessity of constraining gravity and magnetic models. However, typically, they do not demonstrate the individual enhancements that come as a consequence of integrating each constraint into the geophysical model. In this paper, we find that when there are no constraints, it is possible to find an inverse model that is consistent with gravity data, but the model is unrealistic because one sedimentary basin is too deep. Adding a depth-weighting constraint can ensure that the depth is correct, but all other features have the same depth, which is unrealistic. Including densities from a density compilation makes the densities at surface realistic, but the dips are all close to vertical, and the thicknesses are similar, which is unrealistic. In this case, the inversion is believed to have found a local minimum close to the starting model. Reflection seismic data are used to constrain a 2D modeling exercise (on multiple profiles) to determine the geometry of one sedimentary subbasin. These 2D models are then combined to build a realistic 3D starting model. An inversion from this model fixed the densities of each lithology but allowed the thicknesses of the layers to vary. The resulting model is realistic, with the dips and thicknesses away from the seismic constraints being consistent with geologic expectations. Although the fit to the data is much better than the previous model, it is poorer than hoped. If the densities are then allowed to vary within a realistic range of values, the fit can be improved so that the fit to the data and the geologic model are realistic.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.006 |
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".