Hybrid 1D/3D geologically constrained inversion of airborne TEM data
Bibliographic record
Abstract
TEM data are best interpreted in tight integration with geological data. A computer program, VPem1D, has been written to perform 1D TEM inversion in a 3D geological framework. The fact that VPem1D operates on a geological model is advantageous both because it reduces interpretational ambiguity and because it facilitates a variety of inversion styles. If one or more geological units are considered uniform in conductivity, the optimal conductivities can be determined for the entire survey area via homogeneous unit inversion. Moreover, because geological interfaces are captured in the model, geometry inversion can be used to adjust interfacial shape, hence define depth to basement for example. If conductivity varies within geological units, heterogeneous unit inversion can be applied.VPem1D inversion is directly applicable to data from variety of systems including (but not limited to) GEOTEM, TEMPEST, VTEM, Spectrem, SkyTEM, MegaTEM and Hoistem.This paper will illustrate the different inversion options as applied to a variety of case study data sets.
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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) | 0.001 | 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) | 0.004 | 0.001 |
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".