Evolution of a block cave from time-lapse passive source body-wave traveltime tomography
Bibliographic record
Abstract
ABSTRACT Most underground mines are equipped with microseismic monitoring systems that allow the detection, location, and characterization of microseismic events. Microseismic events can be exploited to understand the rock mass response to mining. However, seismicity provides information only for regions that are seismically active. Although some information on nonseismically active regions can be obtained from point measurements and numerical modeling, these methods suffer from limitations of their own. Passive source traveltime body-wave tomography (passive source tomography [PST]) uses information readily collected by microseismic monitoring systems, namely, the P- and/or S-wave traveltimes and microseismic event hypocenter locations. This technique allowes the simultaneous estimation of the velocity distribution between sensors and microseismic events and the correction of microseismic event hypocenter locations. In this paper, we present an application of time-lapse PST to the Northparkes Mines E26 Lift 2 block cave showing that PST can be used to obtain information on evolution and distribution of seismic velocities, leading to a better understanding of stress distribution and redistribution and of rock mass behavior during the development and production phases. In particular, we found that (1) the magnitude of the velocity perturbation varied through time and appeared to be strongly correlated with the intensity of microseismic activity, the mining rate, and the nature of the mining activity, (2) the velocity models provided information that allowed for the inference of the cave geometry and its evolution through time, (3) the stress distributions inferred from the velocity model were not fully consistent with a widely accepted conceptual stress redistribution model, which may reflect the significant influence of rock mass inhomogeneities and the mining sequence, (4) seismicity was found in regions in which velocity was higher and lower than the background velocity, and (5) there was no obvious correlation between geology and velocity distribution and evolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".