Quantitative appraisal of Heliborne and ground-based Time Domain Electromagnetic surveys for uranium exploration – A case study from Rajasthan, India
Bibliographic record
Abstract
SummarySoda-metasomatised and metasediments hosted uranium deposits constitute 18% of the world’s uranium resources. In India, such deposits are identified along the albitite line in the environs of North Delhi Fold Belts in the state of Rajasthan. These are structurally controlled with association of albitisation and pyroxenisation of metasediments, metallic sulphides and carbonaceous phyllites. Uraninite is the dominant mineral phase along with copper, molybdenum and sulphides. The low resistivity of the fractures associated with metallic minerals produces a significant contrast with the host rock, which can be located with electromagnetic methods. High-resolution heliborne geophysical surveys comprising of VTEM, magnetic and gamma-ray spectrometric were conducted to identify uranium deposits in the vicinity of albitite zone. This paper demonstrates the results of heliborne and ground followup geophysical surveys to prioritize targets for uranium exploration. For the first time in India, a ground based Time Domain Electromagnetic survey employing Geonics EM37 system was conducted over one of the target areas delineated based on the VTEM surveys. Interpreted results from ground data correlate well with the spatial locations of the EM conductors delineated from heliborne surveys. Additionally modelling of heliborne and ground data produces comparable physical parameters. The results obtained are extremely useful in planning the boreholes in ongoing exploration programme.
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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 | 0.002 | 0.002 |
| 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) | 0.000 | 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".