Authors: Chambers, DJA; Boltz, MS; Richardson, JR; Finley, SA
Editors: Wesseloo, J
Conference: Eighth International Conference on Deep and High Stress Mining, 28-30 March, Perth
Published: Australian Centre for Geomechanics, Proceedings of the Eighth International Conference on Deep and High Stress Mining, pp.141-154, Perth
Seismic monitoring is an important tool for understanding and mitigating seismic risk in many mining operations, especially those with deep, burst-prone conditions. However, establishing and operating a seismic network that produces quality seismic data can be very expensive due to equipment and labour costs associated with installation and maintenance of seismic stations. Researchers with the National Institute for Occupational Safety and Health are exploring data processing techniques that can improve the quality of a seismic event catalogue that do not require installing additional instrumentation. This paper presents the application of subspace methods to increase event detection capabilities of a surface seismic network monitoring a deep underground metal mine in the northwest of the United States of America. Events recorded on the network from late April to early July 2016 are used to identify similar, lower-magnitude events that occurred in a 15day study period in June of the same year. False detection rates were evaluated by comparing results with a catalogue generated by an in-mine seismic monitoring system, and by visually examining filtered continuous waveform data at the nearest stations to the underground workings. The number of successful event detections more than doubled, with no false detections. However, detected events included production blasts that required screening based on proximity to blasting time. Acceptable estimates of magnitudes and locations for newly detected events were obtained. The application of similar methodologies to other networks may substantially augment event catalogues and provide additional data that can be used in seismic risk analysis to improve mine safety. When continuous waveform data are stored, such processing may be undertaken long after data collection is complete — a particularly valuable capability for investigating emerging stability issues.
Keywords: seismic, event detection, waveform similarity
Keywords: seismic, event detection, waveform similarity
Chambers, DJA, Boltz, MS, Richardson, JR & Finley, SA 2017, 'Application of subspace detection on a surface seismic network monitoring a deep silver mine', in J Wesseloo (ed.), Proceedings of the Eighth International Conference on Deep and High Stress Mining
, Australian Centre for Geomechanics, Perth, pp. 141-154.
Allen, R 1982, ‘Automatic phase pickers: their present use and future prospects’, Bulletin of the Seismological Society of America, vol. 72, no. 6B, pp. 225–242.
Baer, M & Kradolfer, U 1987, ‘An automatic phase picker for local and teleseismic events’, Bulletin of the Seismological Society of America, vol. 77, no. 4, pp. 1437–1445.
Baisch, S, Ceranna, L & Harjes, H 2008, ‘Earthquake cluster: What can we learn from waveform similarity?’, Bulletin of the Seismological Society of America, vol. 98, no. 6, pp. 2806–2814.
Bewick, R, Valley, B, Runnalls, S, Whitney, J & Krynicki, Y 2009, ‘Global approach to managing deep mining hazards’, in M Diederichs & G Grasselli (eds), Proceedings of the 3rd CANUS Rock Mechanics Symposium, vol. 1, Canadian Rock Mechanics Association, American Rock Mechanics Association, Alexandria, pp. 12.
Beyreuther, M, Barsch, R, Krischer, L, Megies, T, Behr, Y & Wassermann, J 2010, ‘ObsPy: A Python toolbox for seismology’, Seismological Research Letters, vol. 81, no. 3, pp. 530–533.
Blake, W & Leighton, F 1969, ‘Recent developments and applications of the microseismic method in deep mines’, The 11th US Symposium on Rock Mechanics, American Rock Mechanics Association, Alexandria.
Chambers, D, Wempen, J, McCarter, M, Pankow, K & Koper, K 2015a, ‘Correlation of newly detected mining induced seismicity with subsidence in a Wyoming mining district’, 2015 SME Annual Conference and Expo and CMA 117th National Western Mining Conference - Mining: Navigating the Global Waters, Society for Mining, Metallurgy and Exploration, Englewood, pp. 192–198.
Chambers, D, Koper, K, Pankow, K & McCarter, M 2015b, ‘Detecting and characterizing coal mine related seismicity in the Western US using subspace methods’, Geophysical Journal International, vol. 203, no. 2, pp. 1388–1399.
Earle, P, Bittenbinder, A, Bogaert, B & Johnson, C 2003, ‘Tune to the worm: Seismic network operation using the USGS Earthworm system’, Observations and Research Facilities for European Seismology, Orfeus Newsletter, vol. 5, no. 1.
Gibbons, S & Ringdal, F 2006, ‘The detection of low magnitude seismic events using array-based waveform correlation’, Geophysical Journal International, vol. 165, no. 1, pp. 149–166.
Gibowicz, SJ & Kijko, A 1994, An introduction to Mining Seismology, Academic Press, New York.
Gledhill, K 1985, ‘An earthquake detector employing frequency domain techniques’, Bulletin of the Seismological Society of America, vol. 75, no. 6, pp. 1827–1835.
Harris, D 2006, Subspace Detectors: Theory, United States Department of Energy.
Jenkins, F, Williams, T & Wideman, C 1990, ‘Rock burst mechanism studies at the Lucky Friday Mine’, The 31th US Symposium on Rock Mechanics (USRMS), American Rock Mechanics Association, Alexandria.
Kiser, E & Ishii, M 2013, ‘Hidden aftershocks of the 2011 Mw 9.0 Tohoku, Japan earthquake imaged with the back projection method’, Journal of Geophysical Research: Solid Earth, vol. 118, no. 10, pp. 5564–5576.
Kubacki, T, Koper, K, Pankow, K & McCarter, M 2014, ‘Changes in mining‐induced seismicity before and after the 2007 Crandall Canyon Mine collapse’, Journal of Geophysical Research: Solid Earth, vol. 119, no. 6, pp. 4876–4889.
Langet, N, Maggi, A, Michelini, A & Brenguier, F 2014, ‘Continuous Kurtosis‐based migration for seismic event detection and location, with application to Piton de la Fournaise Volcano, La Réunion’, Bulletin of the Seismological Society of America, vol. 104, no. 1, pp. 229–246.
Linville, L, Pankow, K, Kilb, D & Velasco, A 2014, ‘Exploring remote earthquake triggering potential across EarthScopes' Transportable Array through frequency domain array visualization’, Journal of Geophysical Research: Solid Earth, vol. 119, no. 12, pp. 8950–8963.
Lomax, A, Satriano, C & Vassallo, M 2012, ‘Automatic picker developments and optimization: FilterPicker—A robust, broadband picker for real-time seismic monitoring and earthquake early warning’, Seismological Research Letters, vol. 83, no. 3, pp. 531–540.
Lu, C, Mai, YW & Xie, H 2005, ‘A sudden drop of fractal dimension: a likely precursor of catastrophic failure in disordered media’, Philosophical Magazine Letters, vol. 85, no. 1, pp. 33–40.
Megies, T, Beyreuther, M, Barsch, R, Krischer, L & Wassermann, J 2011, ‘ObsPy–What can it do for data centers and observatories?’, Annals of Geophysics, vol. 54, no. 1, pp. 47–58.
Mercer, R & Bawden, W 2005, ‘A statistical approach for the integrated analysis of mine-induced seismicity and numerical stress estimates, a case study—Part I: developing the relations’, International Journal of Rock Mechanics and Mining Sciences, vol. 42, no. 1, pp. 47–72.
Poole, D 2014, Linear Algebra: A Modern Introduction, Cengage Learning.
Potvin, Y, 2009, ‘Strategies and tactics to control seismic risks in mines’, Journal of the Southern African Institute of Mining and Metallurgy, vol. 109, no. 3, pp. 177–186.
Schaff, D & Richards, P 2014, ‘Improvements in magnitude precision, using the statistics of relative amplitudes measured by cross correlation’, Geophysical Journal International, vol. 197, no. 1, pp. 335–350.
Schaff, D, Bokelmann, G, Ellsworth, W, Zanzerkia, E, Waldhauser, F & Beroza, G 2004, ‘Optimizing correlation techniques for improved earthquake location’, Bulletin of the Seismological Society of America, vol. 94, no. 2, pp. 705–721.
Schulte-Theis, H & Joswig, M 1993, ‘Clustering and location of mining induced seismicity in the Ruhr basin by automated master event comparison based on dynamic waveform matching’, Computers & Geosciences, vol. 19, no. 2, pp. 233–241.
Sharma, B, Kumar, A & Murthy, V 2010, ‘Evaluation of seismic events detection algorithms’, Journal of the Geological Society of India, vol. 75, no. 3, pp. 533–538.
Slinkard, M, Schaff, D, Mikhailova, N, Heck, S, Young, C & Richards, P 2014, ‘Multistation validation of waveform correlation techniques as applied to broad regional monitoring’, Bulletin of the Seismological Society of America, vol. 104, no. 6, pp. 2768–2781.
Spottiswoode, S & Milev, A 1998, ‘The use of waveform similarity to define planes of mining-induced seismic events’, Tectonophysics, vol. 289, no. 1, pp. 51–60.
Spottiswoode, S, 2010, ‘Mine seismicity: Prediction or forecasting?’, Journal of the Southern African Institute of Mining and Metallurgy, vol. 110, no. 1, pp. 11–20.
Vaezi, Y & Van der Baan, M 2015, ‘Comparison of the STA/LTA and power spectral density methods for microseismic event detection’, Geophysical Journal International, vol. 203, no. 3, pp. 1896–1908.
Vallejos, J & McKinnon, S 2011, ‘Correlations between mining and seismicity for re-entry protocol development’, International Journal of Rock Mechanics and Mining Sciences, vol. 48, no. 4, pp. 616–625.
Waldhauser, F & Ellsworth, W 2000, ‘A double-difference earthquake location algorithm: Method and application to the northern Hayward fault, California’, Bulletin of the Seismological Society of America, vol. 90, no. 6, pp. 1353–1368.
Wang, J & Teng, T 1995, ‘Artificial neural network-based seismic detector’, Bulletin of the Seismological Society of America, vol. 85, no. 1, pp. 308–319.
White, B & Whyatt, J 1999, ‘Role of fault slip on mechanisms of rock burst damage, Lucky Friday Mine, Idaho, USA’, SARES, vol. 99, pp. 2.
Zhao, Y & Jiang, Y 2010, ‘Acoustic emission and thermal infrared precursors associated with bump-prone coal failure’, International Journal of Coal Geology, vol. 83, no. 1, pp. 11–20.