Bibliographic record
Abstract
ABSTRACT: Seismic monitoring systems have long been identified as a key tool for engineers to observe, quantify, and manage the seismic rockmass response to mining. The design of the seismic system, in terms of sensor layout, spacing, and type, is an important first step and is dependent on the specific objectives of seismic monitoring at the mine in question. Once installed and operational, the systems expand and evolve to better meet the requirements of the mine. This paper provides an overview of modern microseismic monitoring systems in mines. We examine trends in the configuration and performance of seismic systems over the years and how advances in technology, processing methods, and computational power have lead to larger and richer datasets than ever before. Differences in geological setting and mining method may inform the monitoring approach and are discussed. The results from this review provide engineers with a way to compare their system configuration and sensitivity to other mines, or for new mines to make an initial estimate of their seismic system monitoring requirements. 1 INTRODUCTION Microseismic monitoring in mines has become ubiquitous in deep, hard rock mines, being one of the fundamental tools used by engineers to identify, quantify, and manage ground control hazards. The earliest examples of seismic monitoring in mines can be traced back to South Africa, Canada, and Australia, going back many decades to the 1970s and 1980s. Over the recent decades, there have been monumental changes in seismic monitoring in mines. These changes are driven by multiple factors. Some of the earliest and potentially most significant improvements were due to technological advances in data transfer speeds. Around the early 2000s, seismic stations started becoming compatible with ethernet, a significant upgrade from serial ports. This provided a massive boost in data transfer speeds, from a maximum of 115 kb/s over serial ports to 100 Mb/s with ethernet. Upgrading existing in-mine infrastructure to the newest technology can be a difficult and time-consuming process, and newer and smaller mines were often the first to benefit from such new technologies. This meant that while new technologies were available, there was often some delay before the benefits could be realized.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".