Natural Hazards

, Volume 83, Supplement 1, pp 133–153 | Cite as

Integration of seismic and image data processing for rockfall monitoring and early warning along transportation networks

  • Panagiotis Partsinevelos
  • George Kritikakis
  • Nikos Economou
  • Zach Agioutantis
  • Achilleas Tripolitsiotis
  • Stelios Mertikas
  • Antonis Vafidis
Original Paper


The occurrence of rockfall incidents on the transportation network may cause injuries, and even casualties, as well as severe damage to infrastructure such as dwellings, railways, road corridors, etc. Passive protective measures (i.e., rockfall barriers, wire nets, etc.) are mainly deployed by operators of ground transport networks to minimize the impact of detrimental effects on these networks. In conjunction with these passive measures, active rockfall monitoring should ideally include the magnitude of each rockfall, its initial and final position, and the triggering mechanism that might have caused its detachment from the slope. In this work, the operational principle of a low-cost rockfall monitoring and alerting system is being presented. The system integrates measurements from a multi-channel seismograph and commercial cameras as the primary equipment for event detection. A series of algorithms analyze these measurements independently in order to reduce alarms originated by surrounding noise and sources other than rockfall events. The detection methodology employs two different sets of algorithms: Time–frequency analyses of the rockfall event’s seismic signature are performed using moving window pattern recognition algorithms, whereas image processing techniques are utilized to deliver object detection and localization. Training and validation of the proposed approach was performed through field tests that involved manually induced rockfall events and recording of sources (i.e., passing car, walking people) that may cause a false alarm. These validation tests revealed that the seismic monitoring algorithms produce a 4.17 % false alarm rate with an accuracy of 93 %. Finally, the results of a 34-day operational monitoring period are presented and the ability of the imaging system to identify and exclude false alarms is discussed. The entire processing cycle is 10–15 s. Thus, it can be considered as a near real-time system for early warning of rockfall events.


Rockfall monitoring Seismic signature Multi-temporal imagery datasets STA/LTA 



Part of this work has been performed under the framework of the “Cooperation 2011” project ISTRIA (11_SYN_9_1389) funded from the Operational Program “Competitiveness and Entrepreneurship” (co-funded by the European Regional Development Fund (ERDF)) and managed by the Greek General Secretariat for Research and Technology. The authors would also like to acknowledge the support provided by the Chania Municipality for the concession of the infrastructure for instrument housing, Dr. A. Daskalakis for developing the modified moving window triggering algorithm, Ms. Vlachou for developing the data management system and Ms. Kobitsaki for her assistance during field experiments.


  1. AgilFence, AgilFence: perimeter intrusion detection system. ccessed 4 April 2016
  2. Agioutantis Z, Mertikas S, Steiakakis C, Daskalakis A, Tripolitsiotis A, Krtitikakis G, Apostolou E, Kaplanidis G (2014) Rockfall monitoring system for improving road safety. In: Alejano R, Perucho Á, Olalla C, Jiménez R (eds) Rock engineering and rock mechanics: Structures in and on rock masses. CRC Press, Taylor & Francis Group, pp 965–970. doi: 10.1201/b16955-166
  3. Arosio D, Longoni L, Papini M, Scaioni M, Zanzi I, Alba M (2009) Towards rockfall forecasting through observing deformation and listening to microseismic emissions. Nat Hazards Earth Syst Sci 9:1119–1131CrossRefGoogle Scholar
  4. Baillard C, Crawford WC, Ballu V, Hibert C, Mangeney A (2014) An automatic kurtosis-based P- and S-phase picker designed for local seismic networks. Bull Seism Soc Am. doi: 10.1785/0120120347 Google Scholar
  5. Below R, Wirtz A, Guha-Sapir D (2009) Working paper: Disaster category classification and peril terminology for operational purposes, centre for research on the epidemiology of disasters. Accessed 27 June 2016
  6. Carrea D, Abellan A, Chantry R, Pedrazzini A, Jaboyedoff M (2013) Monitoring rockfall failure deformation in an active quarry Switzerland. In: Geophysical research abstracts, vol 15, EGU2013-11247, EGU General Assembly. Available online at:
  7. Collins D-S, Toya Y, Hosseini Z, Trifu C-I (2014) Real time detection of rock fall events using a microseismic railway monitoring system. In: GeoHazards 6, Kingston, Canada. Available on line at:
  8. Coviello V, Arattano M, Turconi L (2015) Detecting torrential processes from a distance with a seismic monitoring network. Nat Hazards 78:2055–2080. doi: 10.1007/s11069-015-1819-2 CrossRefGoogle Scholar
  9. Economou N, Kritikakis G, Tripolitsiotis A, Partsinevelos P, Vlachou G, Agioutantis A, Vafidis A (2015) Seismic monitoring for automatic rockfall detection along transportation corridor. European Association of Geoscientists and Engineers, 8th Congress of the Balkan Geophysical Society, doi:  10.3997/2214-4609.201414214
  10. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861–874. doi: 10.1016/j.patrec.2005.10.010 CrossRefGoogle Scholar
  11. Flash P (2003) The Geometry of ROC space: understanding machine learning metrics through ROC isometrics. In: Proceedings of the twentieth international conference on machine learning (ICML-2003). Washington, pp 226–233Google Scholar
  12. Gholamy S, Javaherian A, Ghods A (2008) Automatic detection of interfering seismic wavelets using fractal methods. J Geophys Eng 5:338–347CrossRefGoogle Scholar
  13. Hand DJ (2009) Measuring classifier performance: a coherent alternative to the area under the ROC curve. Mach Learn 77:103–123. doi: 10.1007/s10994-009-5119-5 CrossRefGoogle Scholar
  14. Helmstetter A, Garambois S (2010) Seismic monitoring of Sechilienne Rockslide (French Alps): analysis of seismic signals and their correlation with rainfalls. J Geophys Res 115(F3):F03016CrossRefGoogle Scholar
  15. Hibert C, Mangency A, Grandjean G, Shapiro M-N (2011) Slope instabilities in Dolomieu crater, Reunion Island: from seismic signals to rockfall characteristics. J Geophys Res 116:F04032CrossRefGoogle Scholar
  16. Hibert C, Mangeney A, Grandjean G, Bailard C, Rivet D, Shapiro N-M, Satriano C, Maggi A, Boisser P, Ferrazzini V, Crawford W (2014) Automated identification, location, and volume estimation of rockfalls at Piton de la Fournaise volcano. J Geophys Res Earth Surf 119(5):1082–1105CrossRefGoogle Scholar
  17. Impact Sentinel, Impact Sentinel: Remote monitoring of GEOBRUGG-rockfall protection systems. Accessed 2 April 2016
  18. Iwasaki T (2002) Extended time-term method for identifying lateral structural variations from seismic refraction data. Earth Planets Space 54:663–677CrossRefGoogle Scholar
  19. Joyce K-E, Samsonov S-V, Levick S-R, Engelbrecht J, Belliss S (2014) Mapping and monitoring geological hazards using optical, LiDAR, and synthetic aperture RADAR image data. Nat Hazards 73:137–163CrossRefGoogle Scholar
  20. Kromer R, Hutchinson J, Lato M, Gauthier D, Edqards T (2015) Predicting rockfall occurrence remotely in an operational rail corridor. In: Geophysical research abstracts, vol 17, EGU2015-8237. EGU General Assembly. Available online at:
  21. Levy C, Jongmans D, Baillet L (2011) Analysis of seismic signals recorded on a prone-to-fall rock column (Vercors massif, French Alps). Geophys J Int 186:296–310CrossRefGoogle Scholar
  22. Park CB, Miller RD, Xia J (1999) Multichannel analysis of surface waves. Geophysics 64(3):800–808. doi: 10.1190/1.1444590 CrossRefGoogle Scholar
  23. Partsinevelos P, Mertikas S, Agioutantis Z, Tsioukas V, Tripolitsiotis A, Zervos P (2014) Rockfall detection along road networks using close range photogrammetry. In: Proceedings of SPIE 9229, second international conference on remote sensing and geoinformation of the environment (RSCy2014), 92291 M (12 August 2014); doi:  10.1117/12.2068787
  24. Partsinevelos P, Kallimani C, Tripolitsiotis A (2015) Multi-temporal change image inference towards false alarms reduction for an operational photogrammetric rockfall detection system. In: Proceedings of SPIE 9535, third international conference on remote sensing and geoinformation of the environment (RSCy2015), 95351R (June 19, 2015); doi: 10.1117/12.2199736
  25. Powers MWD (2011) Evaluation: from precision, recall and F-factor to ROC, informedness, markedness and correlation. J Mach Learn Technol 2(1):37–63Google Scholar
  26. Rydelek P, Pujol J (2004) Real-time seismic warning with a two-station subarray. Bull Seismol Soc Am 94(4):1546–1550CrossRefGoogle Scholar
  27. Sättele M, Bründl M, Straub D (2015) Reliability and effectiveness of early warning systems for natural hazards: concept and application to debris flow warning. Reliab Eng Syst Saf 142(2015):192–202. doi: 10.1016/j.ress.2015.05.003 CrossRefGoogle Scholar
  28. Schenato L, Palmieri L, Gruca G, Iannuzzi D, Marcato G, Pasuto A, Galtarossa A (2012) Fiber optic sensors for precursory acoustic signals detection in rockfall events. J Eur Opt Soc Rapid Publ 7:12048. doi: 10.2971/jeos.2012.12048 CrossRefGoogle Scholar
  29. Tripolitsiotis A, Daskalakis A, Mertikas S, Hristopulos D, Agioutantis Z, Partsinevelos P (2015) Detection of small-scale rockfall incidents using their seismic signature. In: Proceedings of SPIE 9535, third international conference on remote sensing and geoinformation of the environment (RSCy2015), 953519 (19 June 2015); doi:  10.1117/12.2192591
  30. Trnkoczy A (2002) Understanding and parameter setting of STA/LTA trigger algorithm. In: Bormann P (ed) IASPEI new manual of seismological observatory practice (NMSOP), vol 2. Deutsches GeoForschungsZentrum, Potsdam, p 119Google Scholar
  31. Turcotte D, Abaimov S, Shcherbakov R, Rundle J (2007) Nonlinear dynamics of natural hazards. In: Tsonis AA, Elsner JB (eds) Nonlinear dynamics in geosciences. Springer, New York, pp 557–580CrossRefGoogle Scholar
  32. Vilajosana I, Surinach E, Abellan A, Khazaradze G, Garcia D, Llosa J (2008) Rockfall induced seismic signals: case study in Montserrat Catalonia. Nat Hazards Earth Syst Sci 8:805–812CrossRefGoogle Scholar
  33. Weir-Jones Engineering Consultants Ltd (2015) System and method for detecting rockfall, Patent No.: US2015285927 (A1). European Patent Office. Accessed: 14 Nov 2015Google Scholar
  34. Wieczorek G-F, Snyder J-B (2009) Monitoring slope movements. In: Young R, Norby L (eds) Geological monitoring. Geological Society of America, Boulder, pp 245–271. doi: 10.1130/2009.monitoring(11) Google Scholar
  35. Withers M, Aster R, Young C, Beiriger J, Harris M, Moore S, Trujillo J (1998) A comparison of select trigger algorithms for automated global seismic phase and event detection. Bull Seism Soc Am 88:95–106Google Scholar
  36. Zhang J, Toksozf N-M (1998) Nonlinear refraction traveltime tomography. Geophysics 63(5):1726–1737CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Geodesy and Geomatics Engineering Lab, School of Mineral Resources Engineering, Technical University of CreteUniversity CampusChaniaGreece
  2. 2.Applied Geophysics Lab, School of Mineral Resources Engineering, Technical University of CreteUniversity CampusChaniaGreece
  3. 3.Department of Mining EngineeringUniversity of KentuckyLexingtonUSA
  4. 4.Space Geomatica LtdChaniaGreece

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