Observation of Rockfall in the Thermal Infrared
Bibliographic record
Abstract
Rockfalls pose a significant risk to personnel and equipment in open pit mines, yet there is currently no widely adopted tool for the detection and real-time monitoring of these hazards. This paper explores the use of thermal infrared cameras to observe, detect, and record rockfall events in surface mining operations, with the aim of protecting mine workers from the dangers of rockfalls. The primary objective is to determine the effectiveness of thermal cameras in detecting rockfalls in a range of environmental conditions. A mobile monitoring platform (MMP) was developed and equipped with a variety of long-wave infrared (LWIR) thermal imaging systems, including both scientific and security-grade cameras. Data have since been collected from nine open pit mining operations across the western United States and southern British Columbia, Canada. Six thermal cameras have been deployed and determined effective in detecting rockfall across a temperature range from − 27 °C to 52 °C. Research findings confirm the utility of thermal infrared imagers in rockfall detection throughout the diurnal cycle (24 h/day), enhancing situational awareness for miners and the potential for integration into geotechnical slope monitoring systems. It was also observed that falling blocks smaller than camera pixel resolution can be detected from thermal video due to temperature/emissivity changes resulting from scours, craters, and dust plumes made by the blocks as they descended slopes. This paper demonstrates LWIR thermal cameras' practical applications and limitations for rockfall detection in various geologic and climate conditions, provides recommendations for collecting and analyzing rockfall-related thermal imaging data, and outlines a path forward for the development of rockfall detection algorithms.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".