Fast 3D inversion of “total field” resistive limit TEM data
Bibliographic record
Abstract
Rapid interpretation of transient electromagnetic (TEM) data sets is highly desirable for timely decision-making in exploration. However, full solution 3D inversion of TEM data sets is often unpractically slow. Therefore, a fast approximate 3D TEM inversion scheme has been developed for time-integrated (resistive limit) data. The resistive limits are amenable to linear 3D magnetic inversion, which is up to 100 times faster than “rigorous” 3D TEM inversion. The resistive limit inversion scheme is suitable for airborne, ground, and downhole TEM, both dB/dt and B-field. Its efficacy is illustrated here via application to a heli-borne sub-audio magnetic (HeliSAM) data set recorded over the Lalor Zn-Cu-Au VMS deposit in Manitoba, Canada. The response from the deposit is clear in the “total field” EM (TFEM) data even though the mineralisation is very deep, extending from depth 575m to over 1100m. A three-stage inversion of resistive limits derived from the TFEM rapidly defined a 3D conductor below the uppermost pyrite-sphalerite lenses, enclosing a volume containing mainly pyrrhotite-chalcopyrite stringer sulphides. Total inversion time was less than one minute on a notebook PC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".