3D Gauss-Newton inversion of surface-borehole TEM data
Bibliographic record
Abstract
Drilling can provide geological information from the subsurface at large depths. However, this information is expensive to obtain and sometimes difficult to interpret because of the lack of lateral continuity in the space between the drillings. Geophysical measurements help to increase the range of investigation around the holes up to a few hundred meters. Borehole electromagnetic methods (BHEM) are particularly well suited for exploring conductive polymetallic ore deposits. To improve the interpretation of the BHEM, a new strategy for 3D inversion of surface-borehole time domain electromagnetic (TEM) data has been developed based on the Gauss-Newton method. Starting the inversion from a uniform half-space model and searching for anomaly zones, combining prior knowledge the anomaly zones are delineated using the isosurface and trace envelope, and then the initial model is modified for the next step of inversion. This iterative inversion process continues until the best fit between TEM observed and predicted data is obtained from the inversion model. The tests on synthetic models and on one field case study show better model resolution and faster convergence of the inversion than the inversion results from a uniform initial model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".