Automatic recognition and classification of multi-channel microseismic waveform based on DCNN and SVM

Abstract The recognition and classification of the multi-channel microseismic waveform are important for mine hazard prediction. It is widely used to design the corresponding waveform feature for recognition and classification of the microseismic waveform by hand. The process of designing features manually is arduous and the results of recognition and classification are not ideal. In this paper, we propose a method combining Deep Convolutional Neural Networks (DCNN) with Support Vector Machine (SVM) for identifying the microseismic waveform automatically. We constructed a DCNN structure to train the optimal weight model named the DCNN-Model. The DCNN-Model is used as a tool for extracting features from multi-channel waveforms. After combining the extracted features, we used SVM to classify multi-channel waveforms. We compared the outputs of other classifiers, such as Random Forest and k-Nearest Neighbor (KNN). To extend the dataset of DCNN training and extract the essential characteristics of waveform images more accurately, we pre-process the raw data by means of filtering and de-nosing. The experiment shows that the recognition and classification method is of practical value, and the accuracy rate can reach as high as 98.18%.

[1]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[2]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[3]  Tao Wang,et al.  End-to-end text recognition with convolutional neural networks , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[4]  Geoffrey E. Hinton,et al.  Replicated Softmax: an Undirected Topic Model , 2009, NIPS.

[5]  Jeffrey F. Tan,et al.  Classification of microseismic events from bitumen production at Cold Lake, Alberta , 2007 .

[6]  Pierre Gaillard,et al.  Detection and classification of seismic events with progressive multi-channel correlation and hidden Markov models , 2015, Comput. Geosci..

[7]  Qiang Chen,et al.  Network In Network , 2013, ICLR.

[8]  Xibing Li,et al.  Discriminant models of blasts and seismic events in mine seismology , 2016 .

[9]  Xue-wen Chen,et al.  Big Data Deep Learning: Challenges and Perspectives , 2014, IEEE Access.

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

[11]  Luca Maria Gambardella,et al.  Deep, Big, Simple Neural Nets for Handwritten Digit Recognition , 2010, Neural Computation.

[12]  Yong-Heng Zhao,et al.  Random forest algorithm for classification of multiwavelength data , 2009 .

[13]  Mu-Chen Chen,et al.  Credit scoring with a data mining approach based on support vector machines , 2007, Expert Syst. Appl..

[14]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[15]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[16]  Mohamed F. Abdelwahed,et al.  SGRAPH (SeismoGRAPHer): Seismic waveform analysis and integrated tools in seismology , 2012, Comput. Geosci..

[17]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[18]  Nikos Mastorakis,et al.  Implementation of SYMLET wavelets to removal of Gaussian additive noise from speech signal , 2011 .

[19]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[20]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[21]  Wen Jin,et al.  The improvements of BP neural network learning algorithm , 2000, WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000.

[22]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[23]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.