3D modelling and inversion of audio-magnetotelluric (AMT) data from McArthur River, Athabasca Basin, using unstructured tetrahedral grids
Bibliographic record
Abstract
The main aim of this study is to do trial-and-error 3D forward modelling and inversion for the AMT data collected in the McArthur River area of the Athabasca Basin. For the forward modelling and inversion, the ideas are not new, but the tools used in this study have not been used previously for the corresponding AMT data set. In the McArthur River area, the P2 fault is a thrust fault in the basement formed within pelitic gneiss, and the mineralization of the unconformity-type uranium found in the Athabasca Basin is often related to the P2 fault - which is originally a graphitic unit. Different geophysical and geological surveys have provided information about parts of the substructure; however, none of the studies so far presented have provided a better understanding of the P2 fault of the McArthur River mine area at depth. All previous studies, including electrical and electromagnetic (EM) surveys, had pointed out the increasing need for imaging the deep parts of the P2 fault. Consequently, a natural source method, the Audio-Frequency Magnetotelluric (AMT) method, was performed in 2002 within the scope of EXTECH IV (EXploration science and TECHnology) project by other researchers to image greater depths at low costs. In this study, a synthetic model is first created by trial and error based on previous studies. The accuracy of the model is checked by comparing the calculated apparent resistivity and phase values with the measurements. According to the results obtained from the forward modelling calculations, the accuracy of the model is satisfactory. iii From a data inversion point of view, this study consists of four inversions. The first is the inversion of the synthetic data for the model constructed by the trial-and-error forward modelling. This allows for the capabilities of the inversion process to be assessed. The other three are the inversions of the real data. For the synthetic data inversion, ten frequencies were used, and the data were fit successfully. The real data inversions were performed using three different forms of the data uncertainties. In the first scenario, the variances estimated from the data processing were considered as uncertainties. In the other two real-data inversions, uncertainties of 3% and 5% were used. Results of the real-data inversion were compared to those of previous inversion studies. The results from all three real-data inversions show good consistency with those of earlier studies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".