Rock damage assessment in a large unstable slope from microseismic monitoring - MMG Century mine (Queensland, Australia) case study

Salvoni, Michele; Dight, Phil

doi:10.1016/j.enggeo.2016.06.002

Cited by 62 publications

(23 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of those, approximately 5% were recorded below the lowest part of the current pit (880RL) and they were most likely the result of stress changes related to the removal of broken rock after blasting and excavation (Lynch et al 2005). The analysis of the microseismic data revealed a strong relationship between the temporal and spatial style of activity for different areas of the slope, in the response to the triggering input (rainfall events), (Salvoni & Dight 2016). Events in the pit wall area were recorded from the 15 February 2014, mainly behind the CBX block.…”

Section: Group Amentioning

confidence: 99%

“…It appeared that movements were reduced by the presence of the CBX block, which was acting as a natural buttress whilst slowly being pushed out in a toppling or rotation manner (i.e. active passive wedge), (Salvoni & Dight 2016). With the progressive rotation of the CBX, fractures were detected behind the block, followed by its partial detachment from the wall (Figure 4(c)).…”

Section: Instability Reactivationmentioning

confidence: 99%

“…This can be related to the fact that there is a disconnection between deformation at surface and at depth. As discussed in Salvoni and Dight (2016), the large deformations experienced on the wall may be caused by a shallow failure moving by creeping and attributed to progressive weathered shales. The seismicity recorded would then be related to a deeper possible failure, still in development.…”

Section: Figure 5 Seismic Events Processed By Group A; Events Markermentioning

confidence: 99%

See 2 more Smart Citations

Microseismic data — a comparison between routine trigger method and continuous data processing

Salvoni

Dight

2016

Proceedings of the First Asia Pacific Slope Stability in Mining Conference

View full text Add to dashboard Cite

Over the years there have been several attempts to undertake routine real-time microseismic monitoring of open pit mine slopes. This technique has been commonly used in underground operations to manage induced seismicity and rockburst. However, the microseismic monitoring in open pits is still experimental and further studies are required. In this paper, we analysed the data from MMG Century mine where, in November 2013, a microseismic system was installed in order to monitor a large scale unstable slope. Design of the system and installation of the instruments were performed by the Institute of Mine Seismology. The seismic events were recorded, based both on a triggered scheme and in continuous mode. As part of our research project, data was given to four independent groups to be analysed and provide their own results. One group applied a routine method using the triggered data, manually processed them and made them available for the engineers on site within 10 minutes. The other three groups later reanalysed the data using both triggered and continuous waveform. Our work compared the different results obtained, and highlighted some of the key points engineers should be aware of in the design and implementation of a microseismic system in open pit mines.

show abstract

Section: Group Amentioning

confidence: 99%

Section: Instability Reactivationmentioning

confidence: 99%

Section: Figure 5 Seismic Events Processed By Group A; Events Markermentioning

confidence: 99%

See 1 more Smart Citation

Microseismic data — a comparison between routine trigger method and continuous data processing

Salvoni

Dight

2016

Proceedings of the First Asia Pacific Slope Stability in Mining Conference

View full text Add to dashboard Cite

show abstract

“…In laboratory rock tests, in situ rock excavation, and a lot of other rock engineering, signals of rock fracture events are often mixed with other signals such as environmental noise, impact and vibration, and blast signal. When these signals are monitored by microseismic or acoustic emission machines [1][2][3][4], the presence of jamming signals, especially blast signals, may result in the wrong interpretation, for example, erroneous state evaluation and disaster prediction [5,6]. Consequently, it is necessary to ensure a clean database of rock fracture signals.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning

Zhou

Cheng

Cai

et al. 2018

Shock and Vibration

View full text Add to dashboard Cite

The automatic discrimination of rock fracture and blast events is complex and challenging due to the similar waveform characteristics. To solve this problem, a new method based on the signal complexity analysis and machine learning has been proposed in this paper. First, the permutation entropy values of signals at different scale factors are calculated to reflect complexity of signals and constructed into a feature vector set. Secondly, based on the feature vector set, back-propagation neural network (BPNN) as a means of machine learning is applied to establish a discriminator for rock fracture and blast events. Then to evaluate the classification performances of the new method, the classifying accuracies of support vector machine (SVM), naive Bayes classifier, and the new method are compared, and the receiver operating characteristic (ROC) curves are also analyzed. The results show the new method obtains the best classification performances. In addition, the influence of different scale factor q and number of training samples n on discrimination results is discussed. It is found that the classifying accuracy of the new method reaches the highest value when q = 8–15 or 8–20 and n=140.

show abstract

“…Combining (15) and (16), we can get the specific measure of AEC cost function [46,47], collecting monitoring data from Apr. 2017 to Oct. 2017, as shown in Figure 7.…”

mentioning

confidence: 99%

Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open‐Pit Mine Slope

Jiang

Lian

et al. 2018

Complexity

View full text Add to dashboard Cite

With the diversification of pit mine slope monitoring and the development of new technologies such as multisource data flow monitoring, normal alert log processing system cannot fulfil the log analysis expectation at the scale of big data. In order to make up this disadvantage, this research will provide an ensemble prediction algorithm of anomalous system data based on time series and an evaluation system for the algorithm. This algorithm integrates multiple classifier prediction algorithms and proceeds classified forecast for data collected, which can optimize the accuracy in predicting the anomaly data in the system. The algorithm and evaluation system is tested by using the microseismic monitoring data of an open-pit mine slope over 6 months. Testing results illustrate prediction algorithm provided by this research can successfully integrate the advantage of multiple algorithms to increase the accuracy of prediction. In addition, the evaluation system greatly supports the algorithm, which enhances the stability of log analysis platform.

show abstract

Rock damage assessment in a large unstable slope from microseismic monitoring - MMG Century mine (Queensland, Australia) case study

Cited by 62 publications

References 12 publications

Microseismic data — a comparison between routine trigger method and continuous data processing

Microseismic data — a comparison between routine trigger method and continuous data processing

Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning

Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open‐Pit Mine Slope

Contact Info

Product

Resources

About