Electromagnetic and electrical methods applied to mapping coked coal – A case study from the Bowen Basin, Eastern Australia
Bibliographic record
Abstract
SummaryIn coal mining it is important to know if localised areas of coal have been rendered worthless through direct contact with volcanic intrusions. To avoid dilution, accurate coked coal boundaries are traditionally mapped by drilling.BHP has made significant strides in applying geophysical methods to add value at its coal operations. With respect to the mapping of coked coal BHP have tried a suite of electromagnetic and electrical methods; enjoying success at improving the resolution of coked coal boundaries with significant implications on the amount of drilling required.Recent trials of moving loop and airborne transient electromagnetic (TEM), Sub-audio magnetic (SAM), electrical resistivity imaging (ERI) and induced polarisation (IP) have encouraged the company to ramp up the inclusion of more TEM in its 5-year operational plans.Unconstrained 1D inversion of moving loop TEM data correlated well to drilled intersections of coked coal. This enabled an improved coked coal boundary to be mapped.An interesting observation is contrary conductivity sections obtained from moving loop TEM and ERI, with the latter method failing to identify the strong conductive layer mapped in both TEM and drilling. Possible conductivity anisotropy of the coked coal or the overlying sediments is being considered as a likely explanation for the failure of ERI to map the coked coal.Inversion of SAM TFEM data partially worked to map the coked coal but only when constrained by the TEM inversion results.The signal to noise ratio in the IP data proved to render the data uninterpretable, suggesting that the coked coal is not chargeable, with the graphite being in massive rather than disseminated form.It is concluded that moving loop TEM and helicopter TEM both efficiently map coked coal sufficiently to target reduced drilling programs. Hence, where scales are appropriate, helicopter surveying is ideal to maximise efficiency without compromising resolution or results.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".