Abstract
Α new method based on variational mode decomposition (VMD) is proposed to distinguish between coal-rock fracturing and blasting vibration microseismic signals. First, the signals are decomposed to obtain the variational mode components, which are ranked by frequency in descending order. Second, each mode component is extracted to form the eigenvector of the energy of the original signal and calculate the center of gravity coefficient of the energy distribution plane. Finally, the coal-rock fracturing and blasting vibration signals are classified using a decision tree stump. Experimental results suggest that VMD can effectively separate the signal components into coal-rock fracturing and blasting vibration signals based on frequency. The contrast in the energy distribution center coefficient after the dimension reduction of the energy distribution eigenvector accurately identifies the two types of microseismic signals. The method is verified by comparing it to EMD and wavelet packet decomposition.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Akaike, H., 1971, Information theory and an extension of the maximum likelihood principle: 2nd International Symposium on Information Theory, 267–281.
Allen, R. V., 1978, Automatic earthquake recognition and timing from single traces: Bull. seism.soc.am, 68(5), 1521–1532.
Alvanitopoulos, P. F., Papavasileiou, M., Andreadis, I., and Elenas, A., 2012, Seismic intensity feature construction based on the Hilbert-Huang transform: IEEE Transactions on Instrumentation and Measurement, 61(2), 326–337.
Allmann, B. P., Shearer, P. M., and Hauksson, E., 2008, Spectral discrimination between quarry blasts and earthquakes in Southern California: Bulletin of the Seismological Society of America, 98(4), 2073–7079.
Dong, L. J., Wesseloo, J., Potvin, Y., and Li, X., 2016, Discrimination of mine seismic events and blasts using the fisher classifier, naive Bayesian classifier and logistric regression: Rock Mechanics and Rock Engineering, 49(1), 183–211.
Gaci, S., 2014, The use of wavelet-based denoising techniques to enhance the first-arrival picking on seismic traces: IEEE Transactions on Geoscience and Remote Sensing, 52(8), 4558–4563.
Huang, H. M., Bian, Y. J., Lu, S. J., Jiang, Z. F., and Li, R., 2010, A wavelet feature research on seismic waveforms of earthquakes and explosions: Acta Seismologica Sinica, 32(3), 270–276.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., and Zheng, Q., 1998, The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis: Proceedings of the Royal Society A Mathematical Physical & Engineering Sciences, 454(1971), 903–995.
Jiang, F. X., Yin, Y. M., Zhu, Q. J., Li, S. X., and Yu, Z. X., 2014, Feature extraction and classification of mining microseismic waveforms via multi-channels analysis: Journal of China Coal Society, 39(2), 229–237.
Jia, R. S., Zhao, T. B., Sun, H. M., and Yan, X. H., 2015, Micro-seismic signal denoising method based on empirical mode decomposition and independent component analysis: Chinese Journal of Geophysics, 58(3), 1013–1023. doi:10.6038/CJG20150326
Jia, R. S., Sun, H. M., Peng, Y. J., Liang, Y. Q., and Lu, X. M., 2016, Automatic event detection in low SNR microseismic signals based on multi-scale permutation entropy and a support vector machine: Journal of Seismology, 21(4), 1–14.
Konstantin, D., and Dominique, Z., 2014, Variation mode decomposition: IEEE Transactions on Signal Processing, 62(3), 531–544.
Lu, C. P., Dou, L. M., Wu, X. R., Wang, H. M., and Qin, Y. H., 2005, Frequency spectrum analysis on microseismic monitoring and signal differentiation of rock material: Chinese Journal of Geotechnical Engineering, 27(7), 772–775.
Liu, X. Q., Shen, P., Zhang, L., and Li, Y. H., 2003, Using method of energy linearity in wavelet transform to distinguish explosion or collapse from nature earthquake: Northwestern Seismological Journal, 25(3), 204–209.
Ma, J., Zhao, G. Y., Dong, L. J., Chen, G. H., and Zhang, C. X., 2015, A comparison of mine seismic discriminators based on features of source parameters to waveform characteristics: Shock and Vibration, 2015(1), 1–10.
Shang, X. Y., Li, X. B., Peng, K., Dong, L. J., and Wang, Z. W., 2016, Feature extraction and classification of mine microseism and blast based on EMD_SVD: Chinese Journal of Geotechnical Engineering, 38(10), 1849–1858.
Tang, S. F., Tong, M. M., Pan, Y. X., He, X. M., and Lai, X. S., 2011, Energy spectrum coefficient analysis of wavelet features for coal rupture microseismic signal: Chinese Journal of Scientific Instrument, 32(7), 1522–1527.
Tang, G. J., and Wang, X. L., 2015, Parameter optimized variational mode decomposition method with application to incipient fault diagnosis of rolling bearing: Journal of Xi’an JiaoTong University, 49(5), 73–81.
Wang, B. L., 2018, Automatic pickup of arrival time of channel wave based on multi-channel constraints: Applied Geophysics, 15(1), 118–124.
Wu, X., Qian, J. S., Wang, H. Y., and Qin, H. C., 2014, Study on multi-scale nonlinear feature extraction and signal identification for microseismic signal: Chinese Journal of Scientific Instrument, 35(5), 969–975.
Xie, P., Yang, F. M., Li, X. X., Yang, Y., Chen, X. L., and Zhang, L. T., 2016, Functional coupling analyses of electroencephalogram and electromyogram based on variational mode decomposition-transfer entropy: Acta Physica Sinica, 65(11), 11870–1:9.
Zhu, Q. J., Jiang, F. X., Yu, Z. X., Yin, Y. M., and Lu, L., 2012a, Study on energy distribution characters about blasting vibration and rock fracture microseismic signal: Chinese Journal of Rock Mechanics and Engineering, 31(4), 723–730.
Zhu, Q. J., Jiang, F. X., Yin, Y. M., Yu, Z. X., and Wen, J. L., 2012b, Classification of mine microseismic events based on wavelet-fractal method and pattern recognition: Chinese Journal of Geotechnical Engineering, 34(11), 2036–2042.
Zhao, G. Y., Ma, J., Dong, L. J., Li, X. B., and Chen, G. H., 2015, Classification of mine blasts and microseismic events usingstarting-up features in seismograms: Transactions of Nonferrous Metals Societyof China, 25(10), 3410–3420.
Zhang, M., Zhu, Y. L., Zhang, N., and Zhang, Y. Y., 2016, Feature extraction of transformer partial discharge signals based on varitional mode decomposition and multi-scale permutation entropy: Journal of North China Electric Power University, 43(6), 31–37.
Zhang, X. L., Lu, X. M., Jia R. S., and Kan, S. T., 2018, Micro-seismic signal denoising method based on variational mode decomposition and energy entropy: Journal of China Coal Society, 43(2), 356–363.
Acknowledgements
We wish to thank the reviewers for their constructive comments.
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by the National Key Research and Development program of China (No. 2016YFC0801406), Shandong Key Research and Development program (Nos. 2016ZDJS02A05 and 2018GGX109013) and Shandong Provincial Natural Science Foundation (No. ZR2018MEE008).
Zhang Xing-Li received her B.Eng. (2002) and M.Eng. (2005) in Software Engineering from the Taiyuan University of Technology, and her Ph.D. (2010) in Software Engineering from Shandong University of Science and Technology. She is presently working in the College of Computer Science and Engineering, Shandong University of Science and Technology. Her main research interests are microseismic signal analysis and processing.
Rights and permissions
About this article
Cite this article
Zhang, XL., Jia, RS., Lu, XM. et al. Identification of blasting vibration and coal-rock fracturing microseismic signals. Appl. Geophys. 15, 280–289 (2018). https://doi.org/10.1007/s11770-018-0682-9
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11770-018-0682-9