A source data-driven method for 3D geological modeling in coal mines
Bibliographic record
Abstract
Building accurate 3D geological models relies heavily on large numbers of data.Nowadays, there are plenty of data including geological, 3D seismic, and roadway data in coal mines, and some specialized softwares allow modeling complex geological bodies using these data.However, there are still two main problems: the first is how to use such information in one modeling system because of the characteristics of the heterogeneity between seismic and other geological data and the second is how to use the latest data because all the data in coal mines change frequently with the production and organization activities.A solution is presented in this paper based on a source data-driven method for 3D geological modeling using geological data, roadway, and seismic interpretation results in a complete process system.The processing flows include: data integration, time-depth conversion, and surfacebased modeling.The method not only effectively takes advantage of various data but also will not lose the correcting information from geologic cognition.Meanwhile, the application in Pingshuo shows that the method is in accord with the dynamic production situation of the coal mine.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".