On the quantitative determination of coal seam thickness by means of in-seam seismic surveys
Bibliographic record
Abstract
By means of in-seam seismic (ISS) surveys carried out at Karvina coal mine of OKD, Czech Republic, in two neighbouring panels it is shown how the distribution of coal seam thickness can be investigated if the coal seam consists of almost pure coal. The coal seam under investigation was considerably affected by erosion, which resulted in thickness changes amounting from ∼30 cm up to ∼4.40 m. First, by applying ISS-tomographic inversion to the group travel times for a constant frequency value, the distribution of the related group velocity was determined, which showed the extension of the erosions within the survey areas. By correlating the group velocity distribution with known values of the coal seam thickness along a gate, a relation in terms of a polynomial approximation between these quantities could be derived. This relation is specific for a chosen frequency value and resolves in general only some of the thickness range. For the application presented in this paper, a value of 200 Hz was chosen for the constant frequency value by which a range of coal seam thicknesses from about 100 cm to about 300 cm was resolved. This was the main objective of the surveys. For the investigation of a greater variation of coal seam thickness (or even the complete thickness distribution), the described procedure has to be repeated for different constant frequency values. The final thickness map then has to be combined from the individual results for the different constant frequency values. The thickness range that was investigated by the surveys was in good agreement with the thickness encountered by mining the panel.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".