MICROSEISMIC MONITORING SYSTEM ESTABLISHMENT AND ITS ENGINEERING APPLICATIONS TO LEFT BANK SLOPE OF JINPING I HYDROPOWER STATION
Bibliographic record
Abstract
Many deep cracks and faults,such as f2,f5 and lamprophyres,which may result in huge potential instable blocks,are the most significant factors that influence the stability of the left bank slope of Jinping I Hydropower Station. In order to real-time monitor and analyze microseismicity in deep rock mass of the left slope,an ESG microseismic monitoring system manufactured in Canada was installed in June,2009. It is the first time to introduce microseismic monitoring technique into high steep rock slope engineering in China. On the basis of setting up three-dimensional geological model of the left slope and optimizing the sensor emplacement,the positioning accuracy of the system has been adjusted according to artificial fixed blasting tests. The results show that seismic source location error is less than 12 m in the scope of the sensor array,which demonstrates microseismic monitoring system has a high positioning accuracy. The waveforms analysis and clustering are investigated and different types of waveforms have been distinguished. Moreover,the tempo-spatial distribution regularities of microseismic events are also analyzed. In addition,a preliminary delineation of deep rock mass deformation zone induced by microseismicity in left bank slope is provided. Furthermore,the stress field and potential slip surface of left slope are simulated by RFPA,which is corresponding to the results of site exploration and microseismic monitoring. This research indicates that the design and implementation of microseismic monitoring system can meet the global monitoring of deep rock mass deformation,which could identify the potential rock damage regions and sliding surface. It will provide a reference for grouting and reinforcement of the left bank slope. A new idea on stability analysis of high rock slopes can thus be obtained.
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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".