Getting more out of drillhole televiewer data: geotechnical toolbox edition
Bibliographic record
Abstract
This paper presents three case studies that utilise acoustic televiewer (ATV) survey data to improve the quality and consistency of geomechanical parameters including intact rock strength, joint condition, fracture spacing and rock mass quality. Two methods of leveraging televiewer data for geomechanical characterisation are explored. The first method employs scripted generation of downhole plots which compare average acoustic amplitude and travel time against logged strength and joint condition. The second is a preliminary automated structure identification workflow built on a state-of-the-art computer vision model (YOLOv8) trained to identify open discontinuities. Rock quality designation (RQD) and fracture count are estimated from the model outputs. Case study 1 illustrates an example of how these automated tools can be used to assist in maintaining data quality and consistency across large teams. It highlights the close correlation between acoustic amplitude and logged strength grade, and between travel time and joint condition, and demonstrates how these relationships can be used to identify logging errors and areas of improvement for individual loggers. Case 2 describes a multi-year drilling program where ATV data were used to improve confidence in logged strengths across weathering horizons and demonstrates a particularly strong relationship between acoustic amplitude and Leeb hardness. Case 3 evaluates the performance of the automated structure identification workflow when used for estimating RQD and fracture count, highlighting the tool’s strengths and limitations. These cases demonstrate ATV data’s significant potential for improving the consistency and quality of geotechnical characterisation.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.105 | 0.048 |
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".