Borer GPR Interpretation During Potash Mining
Bibliographic record
Abstract
Ground Penetrating Radar (GPR) is a non-destructive geophysical technique that has been in use at Saskatchewan potash mines for over four decades. The GPR system is an innovative technology used in imaging salt beds above or below a mined room. The borer mounted GPR application has proven to be a reliable tool for mapping the roof beam thickness which is normally a meter from the mine roof to the immediate clay seam above. Utilizing an automated picking algorithm, real-time data interpretation is provided to borer operators to make informed safety decisions. Hence, it’s important that an auto-picking algorithm is adequately tuned to declutter noise and identify geologic features seen within the mine roof.This paper presents a series of studies aimed at understanding and improving data interpretation of the GPR during active mining as geologic variations within the mine roof can lead to GPR data degradation. An approach to this challenge was to develop a robust and intelligent auto-picking algorithm called the Cluster Ratio Derivative (CRD) that utilizes a data reduction technique to improve the signal to noise ratio (SNR) and machine learning to pick the clay seam in the GPR data. Additional work was performed by developing numerical earth models of a potash mine using gprMax. The generated synthetic datasets, also served as testbed in developing the CRD algorithm.The success of this work has led to the implementation of the novel CRD auto-picking algorithm on borer GPR software. The goal is to continue to ensure that meaningful GPR interpretations are provided to operators during active mining.
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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 | 0.000 | 0.000 |
| 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".