Journal of the Geological Society of India

, Volume 92, Issue 6, pp 679–686 | Cite as

Development of Earthquake Event Detection Technique Based on STA/LTA Algorithm for Seismic Alert System

  • Satish KumarEmail author
  • Renu Vig
  • Pawan Kapur


Among natural disasters, earthquake is the most common calamity which result in a wide spectrum of destruction, loses of property and human life. In order to mitigate the damage impact, there is an urgent need to realize a technological solution for vital installations i.e. Seismic alert system (SAS). It is a solution to avert colossal loss as earthquake forecasting is not yet possible and is being devised for regional notification for a possible mitigation while it is in active mode. For designing SAS, a network of seismic sensing node (SSN) is to be configured to detect the seismic activity in the region of interest. SSN discriminates between local noise and seismic event in real time using inbuilt event detection technique. The true seismic event is declared on satisfying the field parameters programmed by the user i.e. short and long duration window, threshold ratio, stability factor, de-threshold ratio, number of threshold channels, sampling rate, pre-event and post-event duration, number of channel and packet size duration. This paper illustrates the design aspects related to the event detection technique incorporated in the seismic node of the alert system and configured around the seismic sensors, 24-bit high resolution digitizers, GPS modules, etc. The results are validated by varying the different field parameters on recorded signal.


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  1. Allen, R.M., and Kanamori, H. (2003) The potential for earthquake early warning in southern California. Science, v.300, pp.786–789.CrossRefGoogle Scholar
  2. Baranov, S.V. (2007) Application of the Wavelet Transform to Automatic Seismic Signal Detection, Izvestiya. Physics of the Solid Earth, v.43(2), pp.177–188. © Pleiades Publishing, Ltd.CrossRefGoogle Scholar
  3. Boschetti, F., Dentith, M.D., and List, R.D. (1996) A fractal-based algorithm for detecting first arrivals on seismic traces. Geophysics, v.61(4), pp.1095–1102CrossRefGoogle Scholar
  4. Bose, M., Wenzel,F. and Erdik, M. (2008) PreSEIS: A Neural Network-Based Approach to Earthquake Early Warning for Finite Faults. Bull. Seismol. Soc. Amer., v.98(1), pp.366–382, doi:10.1785/0120070002CrossRefGoogle Scholar
  5. Botella, F., Rosa-Herranz,J., Ginerc, J.J., Molina, S. and Galiana-Merino, J.J. (2003) A real-time earthquake detector with prefiltering by wavelets. Computers & Geosciences, v.29, pp.911–919CrossRefGoogle Scholar
  6. Brown, H.M., Allen, R.M., Hellweg, M., Khainovski, O., Neuhauser, D. and Souf, A. (2011) Development of the ElarmS methodology for earthquake early warning: Realtime application in California and offline testing in Japan. Soil Dynamics and Earthquake Engineering, v.31, pp.188–200CrossRefGoogle Scholar
  7. Chen, Z. and Stewart, R.R. (2006) A multi-window algorithm for real-time automatic detection and picking of P-phases of microseismic events. CREWES Research Report, v.18 pp. 1–9Google Scholar
  8. Cicerone, R.D., Ebel, J.E. and Britton, J. (2009). A Systematic Compilation of Earthquake Precursors, Tectonophysics, v.476, pp. 371–396CrossRefGoogle Scholar
  9. Dai-Zhi, L., Ren-Ming, W., Xi-Hai, L. and Zhi-Gang, L. (2005) Onset Point Identification of Single-Channel Seismic Signal based on Wavelet Packet Decomposition and AR Model. Chinese Jour. Geophys, v.48(5), pp.1170–1175CrossRefGoogle Scholar
  10. Espinosa-Aranda, J.M., Cuellar, A., Rodriguez, F.H., Frontana, B., Ibarrola, G., Islas, R. and Garcia, A. (2011) The seismic alert system of Mexico (SASMEX): Progress and its current applications. Soil Dynamics and Earthquake Engineering, v.31(2), pp.154–162CrossRefGoogle Scholar
  11. Fletcher, S.K. (1983) Walsh Transforms in Seismic-Event Detection. IEEE Transactions on Electromagnetic Compatibility, v.25, No. 3, pp. 367–369CrossRefGoogle Scholar
  12. Gabarda, S. and Cristobal, G. (2010) Detection of events in seismic time series by time–frequency methods, Special Issue on Time-Frequency Approach to Radar Detection, Imaging, and Classification. IET Signal Processing, v. 4, Issue 4, pp. 413–420 doi: 10.1049/iet-spr.2009.0125CrossRefGoogle Scholar
  13. Ghosh, R., Akula, A., Kumar S. and Sardana, H.K. (2015) Time–frequency analysis based robust vehicle detection using seismic sensor. Jour. Sound and Vibration, v.346, pp.424–434CrossRefGoogle Scholar
  14. Hafez, A.G., Khan, T.A. and Kohda, T. (2009) Earthquake onset detection using spectro-ratio on multi-threshold time–frequency sub-band. Digital Signal Processing, v.19, pp.118–126CrossRefGoogle Scholar
  15. Hafez, A.G., Khan,T.A.,Kohda,T. (2009) Earthquake onset detection using spectro-ratio on multi-threshold time–frequency sub-band. Digital Signal Processing, v.19, pp.118–126CrossRefGoogle Scholar
  16. Han, L., Wong, J., and Bancroft, J.C. (2009) Time picking and random noise reduction on microseismic data. CREWES Res. Report, v.21, pp.1–13Google Scholar
  17. Hsiao, N.C.,Wu, Y.M., Shin, T.C., Zhao, L., and Teng, T.L. (2009) Development of earthquake early warning system in Taiwan. Geophys. Res. Lett., v.36, L00B02, pp. 1–5, doi:10.1029/2008GL036596Google Scholar
  18. Huang, J., Zhou, Q., Zhang, X., Song, E., Li, B. and Xiaobing Yuan, X. (2013) Seismic Target Classification Using a Wavelet Packet Manifold in Unattended Ground Sensors Systems, Sensors (Basel), v.13(7), pp 8534–8550.CrossRefGoogle Scholar
  19. Isfeldt, B. (2017). Inductive power for seismic sensor node US Patent 9595833Google Scholar
  20. Iunio, I., Giorgio, M. and Manfredi, G. (2007) Expected loss-based alarm threshold set for earthquake early warning systems. Earthquake Engg. Struct. Dyn., v.36, pp.1151–1168, DOI: 10.1002/eqe.675CrossRefGoogle Scholar
  21. Kanamori, H. (2005) Real-time seismology and earthquake damage mitigation. Annu. Rev. Earth Planet. Sci., v.33, pp.195–214, doi:10.1146/ Scholar
  22. Kumar, N., Chauhan,V., Dhamodharan, S., Rawat, G., Hazarika, D. and Gautam, P.K.R. (2017). Prominent precursory signatures observed in soil and water radon data at MPGO, Ghuttu for Mw7.8 Nepal Earthquake, Curr. Sci., v.112(5), pp.907–909.Google Scholar
  23. Magotra, N., Ahmed, N. and Chael, E. (1987) Seismic event detection and source location using single-station (three-component) data. Bull. Seismol. Soc. Amer., v.77(3), pp.958–971Google Scholar
  24. Magotra, N., Nalley, D., and Brozek, J. (1992), Seismic Event Detection Using Three-Component Data. IEEE Transactions On Geoscience and Remote Sensing, v.30(3), pp. 642–644CrossRefGoogle Scholar
  25. Marmureanu, Ionescu, C. and Cioflan, C.O. (2011) Advanced real-time acquisition of the Vrancea earthquake early warning system. Soil Dynamics and Earthquake Engineering, v.31, pp.163–169CrossRefGoogle Scholar
  26. McGuire, J.J., Simons, F.J., and Collins, J.A. (2008) Analysis of seafloor seismograms of the 2003 Tokachi-Oki earthquake sequence for earthquake early warning, Geophys. Res. Lett., v.35, L14310, doi:10.1029/2008GL033986CrossRefGoogle Scholar
  27. Munro, K. (2004) Automatic event detection and picking of P-wave arrivals, Automatic Event Detection, CREWES Research Report, v.16, pp. 1–10Google Scholar
  28. Munro, K.A. (2004) Automatic event detection and picking P-wave arrivals: CREWES Research Report, v.18, pp.12.1–12.10Google Scholar
  29. Olson, E. L. and Allen, R. M. (2005) The deterministic nature of earthquake rupture, Nature, v.438, pp.212–215.CrossRefGoogle Scholar
  30. Oth A., Bose, M., Wenzel, F., Köhler, N., Erdik, M. (2010) Evaluation and optimization of seismic networks and algorithms for earthquake early warning–the case of Istanbul (Turkey), Jour. Geophys. Res., v.115, B10311, doi:10.1029/2010JB007447CrossRefGoogle Scholar
  31. Paul Ibanez (2008) An Introduction to Shake Tables for Seismic Testing of Equipment and Glossary of Vibration Terminology, pp.1-44 www.anco SEISMICTESTINGINTRO.pdfGoogle Scholar
  32. Ruud, B. and Husebye, E. (1992) A new three-component detector and automatic single-station bulletin production. Bull. Seismol. Soc. Amer., v.82, pp.221–237.Google Scholar
  33. Satriano, C., Wu, Y.M., Zollo, A. and Kanamori, H. (2011) Earthquake early warning: Concepts, methods and physical grounds. Soil Dynamics and Earthquake Engineering v.31(2), pp.106–118CrossRefGoogle Scholar
  34. Sharma, B.K., Kumar, A. and Murthy, V. M. (2010) Evaluation of Seismic Events Detection Algorithms. Jour. Geol. Soc. India, v.75, pp.533–538CrossRefGoogle Scholar
  35. Sokolov, V., Wenzel, F. and Furumura, T. (2009) On estimation of earthquake magnitude in Earthquake Early Warning systems. Earth Planets Space, v.61, pp.1275–1285CrossRefGoogle Scholar
  36. Trnkoczy, A. (1999) Understanding and parameter setting of STA/LTA trigger algorithm, Information Sheet IS8.1, pp 1–20Google Scholar
  37. Withers, M., Aster, R., Young, C., Beiriger, J., Harris,M., Moore, S. and Trujillo, J. (1998) A Comparison of select Trigger Algorithms for Automated Global Seismic Phase and Event Detection. Bull. Seismol. Soc. Amer., v.88(1), pp.95–106Google Scholar
  38. Wu Y.M. and Kanamori, H. (2008b) Development of an Earthquake Early Warning System Using Real-Time Strong Motion Signals, Sensors, v.8, pp.1–9.CrossRefGoogle Scholar
  39. Wu Yih-Min Hiroo Kanamori Richard M. Allen Egill Hauksson (2007) Determination of earthquake early warning parameters, ôc and Pd, for southern California. Geophys. Jour. Internat., v.170 (2), pp.711–717, DOI: CrossRefGoogle Scholar
  40. Wu, Y.M. (2005) Experiment on an onsite early warning method for the Taiwan early warning system. Bull. Seismol. Soc. Amer., v.95, pp.347–353.CrossRefGoogle Scholar
  41. Wu, Y.M. and Kanamori, H. (2005) Rapid assessment of damaging potential of earthquakes in Taiwan from the beginning of P Waves. Bull. Seismol. Soc. Amer., v.95, pp. 1181–1185.CrossRefGoogle Scholar
  42. Wu, Y.M. and Kanamori, H. (2008) Exploring the feasibility of on-site earthquake early warning using close-in records of the 2007 Noto Hanto earthquake. Earth Planets Space, v.60, pp.155–160CrossRefGoogle Scholar
  43. Wu, Y.M. and Teng, T.L. (2002) A Virtual Subnetwork Approach to Earthquake Early Warning, Bull. Seismol. Soc. Amer., v.92(5), pp. 2008–2018CrossRefGoogle Scholar
  44. Wu, Y.M., Yen, H.Y., Zhao, L., Huang, B.S., and Liang, W.T. (2006) Magnitude determination using initial P waves: A single-station approach, Geophys. Res. Lett., v.33(5), pp.1–4, Doi:10.1029/2005GL025395Google Scholar
  45. Zollo, A., Amoroso, O., Lancieri, M., Wu, Y.M. and Kanamori, H. (2010) A threshold-based earthquake early warning using dense accelerometer networks. Geophys. Jour. Internat., v.183, pp. 963–974.CrossRefGoogle Scholar

Copyright information

© Geological Society of India 2018

Authors and Affiliations

  1. 1.CSIR-Central Scientific Instruments OrganisationChandigarhIndia
  2. 2.University Institute of Engineering and TechnologyPanjab UniversityChandigarhIndia

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