Application of Correlation Integral and Fractal Dimension in Longwall Mine Safety (original) (raw)
Related papers
The longwall mining is considered to be the best coal mining practices due to vast recovery of coal over other forms of underground as well as opencast mining methods. But the actual scenario is quite opposite in India where productivity form of the longwall mines lags far behind than the desired level. Irregular caving and sudden rock bursts, which are very hazardous for mine workers and equipment. Usually these are major problem faced by bulk of Indian longwall faces and which are due to the presence of thick sandstone beds as overlying strata. Thus to keep an eye on the rock bursts, it is very necessary to monitor the stressed zones in the hanging overlying strata above and behind the panel. Earlier Correlation Integral 'C' and Correlation Fractal Dimension 'Dc' has been very helpful in monitoring the stressed zones for several great earthquakes in past. Following the same way, in the present study we have used the mine-induced microseismic data obtained from the retreating longwall panel using various monitoring instruments to calculate the Correlation Fractal Dimension 'Dc' for monitoring the stress levels and fractures in the overlying strata and also for spatio-temporal forecasting of roof-falls. The variation of blast charge size with Fractal Dimension is also studied. The use of Fractal Dimension has been very effective in obtaining the precursory signatures for roof-fall, thus ensuring safety in the mines.
Engineering Geology, 2019
Mining of coal has always been a very risky task from older times and, is often associated with various fatal accidents in form of rock bursts, gas outbursts, slope/bench failure. Studies carried out by various safety agencies around the world such as MSHA (USA), DGMS (India), State Administration (China), have reported higher fatality rates in underground mines, and are mostly resulting from the sudden roof falls. Most of the roof falls occur suddenly in the running mine due to support failure or adverse strata conditions. The present study focuses on the monitoring of the roof falls in the longwall mine of Central India, as the presence of stable roof has resulted in several roof fall related accidents in various Indian longwall panels including Churcha mine, Kothadi mine and GDK-11A incline. The aforesaid accidents was also responsible for the poor performance of longwall mines in India, which is considered to be one of the best mining practices in majority of the coal producing countries of the world. The paper incorporates the spatial distribution and magnitude of microseismic events released before/during the roof falls and surface blasting in terms of Fractal Dimension as well as b-value. These two parameters helped in getting precursory signatures for spatio-temporal forecasting of minor and major roof falls, as well as helped in monitoring strata behavior during surface blasting. A total of sixty-four minor falls (LF1-LF64), seventy-six major falls (RF1-RF76) and fifteen surface blasting (SB1-SB15). The results showed decreasing trend in the both Fractal Dimension (D) and b-value before roof fall, as microseismic events emitted were highly clustered and had higher magnitude. Whereas, higher Fractal Dimension (D) and lower b-value was seen during surface blasting, when the emitted microseismic events had higher magnitude but were found to be dispersive in nature.
2019
Seismic hazards have become one of the common risks in underground coal mining and their assessment is an important component of the safety management. In this study, a methodology, involving nine fractal dimension-based indices and a fuzzy comprehensive evaluation model, has been developed based on the processed real time microseismic data from an underground coal mine, which allows for a better and quantitative evaluation of the likelihood for the seismic hazards. In the fuzzy model, the membership function was built using a Gaussian shape and the weight of each index was determined using the performance metric F score derived from the confusion matrix. The assessment results were initially characterised as a probability belonging to each of four risk levels (none, weak, moderate and strong). The comprehensive result was then evaluated by integrating the maximum membership degree principle (MMDP) and the variable fuzzy pattern recognition (VFPR). The model parameters of this methodology were first calibrated using historical microseismic data over a period of seven months at Coal Mine Velenje in Slovenia, and then applied to analyse more recent microseismic monitoring data. The results indicate that the calibrated model was able to assess seismic hazards in the mine.
International Journal of Rock Mechanics and Mining Sciences, 2020
Coal permeability is a key issue in CO 2 injection and enhanced coalbed methane (CBM) recovery, and it is determined by the fracture network, which is strongly influenced by mining-induced stress evolutions. For a comprehensive understanding of the size and spatial distribution characteristics of coal fracture networks under different mining-induced stress conditions, a series of laboratory experiments, computed tomography (CT) scans and image analyses focusing on 3D coal fracture systems have been conducted considering the stress conditions induced by three typical mining layouts, i.e., top-coal caving mining (TCM), non-pillar mining (NM) and protective coal-seam mining (PCM). The size and spatial distributions of mining-induced coal microfractures and the anisotropic tortuosity characteristics of coal fracture networks have been quantitatively determined based on fractal theory. The results show that the size distributions of microfracture geometries, the fractal dimensions of fracture size distribution and the tortuosity of the mining-induced coal fracture networks vary according to the mining-induced stress conditions, resulting in differences in the seepage capacity of coal masses. The coal samples subjected to PCM conditions have the highest percentage of large microfractures, and those exposed to NM conditions have the lowest percentage. The fractal dimensions (D a and D d) of the microfracture size distributions of the typical coal specimens decrease in the order of PCM, NM and TCM conditions, and the D a and D d of coal specimens without pre-mining unloading-expansion simulation (PUES) are much lower than those of specimens with PUES. The tortuosity fractal dimensions (δ) of the mining-induced fracture network are spatially anisotropic. The ranges of δ for the coal masses exposed to PCM, NM and TCM conditions are 1.0-1.2, 1.2-1.3 and 1.25-1.4, respectively. Using fractal theory and the measured fracture network data, the anisotropic spatial distribution of coal permeability can be theoretically estimated.
Annals of Geophysics, 2019
New Zealand earthquake that occurred on 15 th July 2009 (Mw 7.8) was analysed using fractal correlation dimension (Dc) and seismic b-value. We have analysed the earthquakes catalog of thirty-five years with a magnitude (mb ≥3.7), in order to observe a crucial information in terms of Dc value fluctuation for the event. The event is preceded by fall and anomalous change in Dc value in the year 2007 about two years prior to the mainshock. A sudden decrease in Dc value with highly clustered events is observed before the mainshock. The low value of Dc is an indicator of clustering and it shows how intermediate size events correlate with one another in the preparation process of this event. Here the low Dc value may be the indicator for high stress developer along the fault to produce large size earthquake. Moreover, we also observed abnormal fluctuation in b-value from 2003. The fractal clustering and scaling of earthquakes are indicated by b-value change prior to strong earthquake as a harbinger of stress correlation in various scales. The event is also marked for that occurred in the periphery of the positive Coulomb stress development, as obtained from three low Dc time windows' events. The drop in Dc value is not a single observation prior to this large event, but such pattern is also seen for other strong events in the study zone. One such well identified strong event is Mw 7.2 (2003) along with low Dc value prior to the event. Thus, stress correlation measured along with these indirect statistical tools gives the clue of selforganization of long wavelength of stress, which was not measured earlier with classical approaches. This type of study may provide a very useful information for hazard mitigation.
Evaluating Hurst Parameters and Fractal Dimensions of Surveyed Dataset of Tailings Dam Embankment
2019
In the mining environment, tailings dam embankment is among the hazards and risk areas. The tailings dam embankment could fail and result to damages to facilities, human injuries or even fatalities. Periodic monitoring of the dam embankment is needed to help assess the safety of the tailings dam embankment. Artificial intelligence techniques such as fractals can be used to analyse the stability of the monitored dataset from survey measurement techniques. In this paper, the fractal dimension (D) was determined using D = 2-H. The Hurst parameters (H) of each monitored prism were determined by using a time domain of rescaled range programming in MATLAB software. The fractal dimensions of each monitored prism were determined based on the values of H. The results reveal that the values of the determined H were all within the threshold of 0 ≤ H ≤ 1 m. The smaller the H, the bigger the fractal dimension is. Fractal dimension values ranging from 1.359 x 10<sup>-4</sup> m to 1.88...
Clustering of mining-induced seismic events in equivalent dimension spaces
Journal of Seismology, 2014
High energy release during seismic events induced by mining operation is one of the major dangers perturbing production in underground mines. In this work, temporal changes of seismic event parameters for one of the Rudna Mine (Poland) panels are investigated. The study aim was to find whether the temporal clustering of smaller events in different parameters can be observed before and after the high energy events (M l ≥3) in the mining panel. The method chosen for analysis was the study of temporal variation of fractal dimension of the seismic events parameter sets composed from: the interevent epicentral distance (dr), logarithm of seismic energy (lE), and interevent energy coefficient (dlE), which is the absolute difference between logarithms of energy of two consecutive events. Temporal variations study was performed in equivalent dimension (ED) space. The transformation of the seismic source parameters into ED space allowed to estimate and compare the temporal changes of the fractal dimension of different parameter spaces using the same method-correlation fractal dimension, and then easily compare the obtained temporal changes of fractal dimension of different parameter sets. The effect of grouping is expressed by decrease of fractal dimension, which is connected with the similarity of events parameter values. The temporal changes of the fractal dimension of seismicity before the strong induced events would indicate some initiation phase of the process leading to the high energy release. In the case of the studied Rudna Mine panel, the temporal behavior of the fractal dimension values in different parameter spaces before seismic events showed significant changes before three out of four events with CLVD dominant source mechanisms.
Research in Geophysics, 2012
We analyzed statistical properties of earthquakes in western Anatolia as well as the North Anatolian Fault Zone (NAFZ) in terms of spatio-temporal variations of fractal dimensions, p- and b-values. During statistically homogeneous periods characterized by closer fractal dimension values, we propose that occurrence of relatively larger shocks (M >= 5.0) is unlikely. Decreases in seismic activity in such intervals result in spatial b-value distributions that are primarily stable. Fractal dimensions decrease with time in proportion to increasing seismicity. Conversely, no spatiotemporal patterns were observed for p-value changes. In order to evaluate failure probabilities and simulate earthquake occurrence in the western NAFZ, we applied a modified version of the renormalization group method. Assuming an increase in small earthquakes is indicative of larger shocks, we apply the mentioned model to micro-seismic (M<= 3.0) activity, and test our results using San Andreas Fault Zone ...