3D inversion of a single profile MT data acquired on ip anomaly: Case history for mineral exploration in BC, Canada
Bibliographic record
Abstract
<!--!introduction!--><b></b> In this study, we used state-of-the-art 3D inversion for inverting the MT data acquired along a single profile in BC, Canada. The objective of the field work was to reveal the deep mineralization structure which was recovered at the near surface by using resistivity and IP methods performed by third party contractors. We acquired full tensor MT data on 11 sites along the profile that has the strongest IP anomaly. Time series data recorded for average 10 h duration. Remote reference processing was applied to the time series data using a remote site located 50 km southeast of the survey area. We estimated the full impedance tensor as well as the vertical magnetic transfer function. The results of the 2D inversion applied to the MT data were consistent with those of the DC-IP survey. We also applied 3D inversion to the single profile data to compare it with the 2D results and also to evaluate the resistivity distribution around the profile area. In addition to the deep conductor that created the strongest anomaly, we also recovered another deep conductivity anomaly 300 m North of the profile. We tested the accuracy of this anomaly with a sensitivity matrix and also tested the 3D inversion results using a synthetic modeling study. It can be seen from this study that MT is a very important tool for understanding mineralization structure and is better used as an initial method for mineral exploration to localize an area for other geophysical methods such as DC/IP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".