APPLICATION OF AN INNOVATIVE AEM SYSTEM FOR MAPPING HAZARDS AND WATER RESOURCES IN OIL AND GAS FIELDS
Bibliographic record
Abstract
Mira Geoscience has collaborated with SkyTEM Canada Inc. to produce this case study using public Airborne Electromagnetic (AEM) data collected by Geoscience BC in partnership with members of the Horn River Basin Producers Group. The objective of the AEM survey at the outset was to delineate possible sources of near surface groundwater thought to be contained in quaternary paleochanels. Modelling and interpretation of the data has resulted in imaging of subsurface resistivity features thought to represent these paleochannels. Throughout the course of the project other applications of the dataset have become apparent during the interpretive process. These applications include: detection of shallow gas and structures that may confine gas in the near surface (clay caps), explanation of artesian water in well d-66-f and prediction of further artesian water flow throughout the property, and detection of near surface coarse materials for engineering applications such as road and drill pad construction. The case study illustrates the interpretive power of combining AEM models with seismic interpretation as well as the advantages that low noise and high resolution multi moment airborne electromagnetic data acquisition systems and advanced EM processing bring to the interpretive process.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".