The spatial and temporal assessment of clustered and time-dependent seismic responses to mining
Bibliographic record
Abstract
The phenomenon of seismicity is observed in many hard rock underground mines around the world. The potential for seismic events to damage underground excavations can create a significant hazard to mining personnel, equipment, and infrastructure. The management of seismic hazard is an essential component in minimising the political, social, and economic risks associated with mining. The effective management of seismic hazard is underpinned by a sufficient understanding of the magnitude, spatial, and temporal characteristics of seismicity. These characteristics of seismicity are controlled by causative seismic source mechanisms within a mine and are related to stress conditions, rock mass strength, excavations, geology, and geological features. This paper considers spatially clustered seismicity, which is generated by a timedependent rock mass failure process. Seismicity of this nature is routinely observed following blasting or large seismic events and is referred to as a ‘seismic response’ within this paper. There are numerous interrelated factors that can influence the characteristics of seismic responses and this makes it difficult to establish meaningful correlations with causative processes. Furthermore, the management of seismic response hazard has the tendency to rely on the sitespecific experience, which has inherent limitations. These areas of research can be partly addressed by the quantification of seismic responses that allows for the development of an objective understanding of seismic response hazard. This paper presents a general outline of a recently published methodology for the assessment of seismic responses that concurrently examines the spatial and temporal characteristics of these responses. A general discussion on the major considerations when applying the method is provided in this paper. The benefits of the quantification of seismic responses are illustrated by several case studies. These studies assess individual responses and consider the historical distribution of response characteristics for a mining environment.
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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 | 0.003 |
| 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 | 0.001 |
| 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".