A Comparison of Mine Seismic Discriminators Based on Features of Source Parameters to Waveform Characteristics

To find efficient methods for classifying mine seismic events, two features extraction approaches were proposed. Features of source parameters including the seismic moment, the seismic energy, the energy ratio of S- to P-wave, the static stress drop, time of occurrence, and the number of triggers were selected, counted, and analyzed in approach I. Waveform characteristics consisting of two slope values and the coordinates of the first peak and the maximum peak were extracted as the discriminating parameters in approach II. The discriminating performance of the two approaches was compared and discussed by applying the Bayes discriminant analysis to the characteristic parameters extracted. Classification results show that 83.5% of the original grouped cases are correctly classified by approach I, and 97.1% of original grouped cases are correctly classified by approach II. The advantages and limitations pertaining to each classifier were discussed by plotting the event magnitude versus sample number. Comparative analysis shows that the proposed method of approach II not only has a low misjudgment rate but also displays relative constancy when the testing samples fluctuate with seismic magnitude and energy.

[1]  A. Mendecki.  Seismic Monitoring in Mines , 2011 .

[2]  S. Weisberg.  Applied Linear Regression: Weisberg/Applied Linear Regression 3e , 2005 .

[3]  J. Navarro-Pedreño.  Numerical Methods for Least Squares Problems , 1996 .

[4]  D Malovichko.  Discrimination of blasts in mine seismology , 2012 .

[5]  Xibing Li,et al.  A Microseismic/Acoustic Emission Source Location Method Using Arrival Times of PS Waves for Unknown Velocity System , 2013, Int. J. Distributed Sens. Networks.

[6]  A. Kijko,et al.  An introduction to mining seismology , 1994 .

[7]  Jon B. Fletcher,et al.  The partition of radiated energy between P and S waves , 1984 .

[8]  Xibing Li,et al.  An efficient closed-form solution for acoustic emission source location in three-dimensional structures , 2014 .

[9]  Li Xibing,et al.  Three-dimensional analytical solution of acoustic emission or microseismic source location under cube monitoring network , 2012 .

[10]  J. A. Ryder,et al.  A handbook on rock engineering practice for tabular hard rock mines , 1999 .

[11]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[12]  R. P. Young,et al.  Influence of source region properties on scaling relations forM<0 events , 1992 .

[13]  W. Marsden.  I and J , 2012 .

[14]  Xibing Li,et al.  Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers , 2013 .

[15]  Yu Guo-feng.  Comprehensive Analysis Method of Identifying and Calibrating Micro-Seismic Events Attributes in Coal and Rock Mass , 2011 .

[16]  Peter M. Shearer,et al.  Introduction to Seismology , 2019 .

[17]  Theofanis Sapatinas,et al.  Discriminant Analysis and Statistical Pattern Recognition , 2005 .

[18]  Yousef Abolfazlzadeh,et al.  Application of seismic monitoring in caving mines , 2013 .

[19]  S. D. McKinnon,et al.  Logistic regression and neural network classification of seismic records , 2013 .

[20]  Kristina Larsson.  Seismicity in Mines A Review , 2004 .

[21]  Xibing Li,et al.  An Analytical Solution for Acoustic Emission Source Location for Known P Wave Velocity System , 2014 .

[22]  Wen Jing-lin,et al.  Classification of mine microseismic events based on wavelet-fractal method and pattern recognition , 2012 .

[23]  Ju Ma,et al.  Three-dimensional analytical solution of acoustic emission source location for cuboid monitoring network without pre-measured wave velocity , 2015 .

[24]  S. Weisberg.  Applied Linear Regression , 1981 .

[25]  Jiang Fu-xin.  Feature extraction and classification of mining microseismic waveforms via multi-channels analysis , 2014 .