Abstract
The Earthquake Network research project implements a crowdsourced earthquake early warning system based on smartphones. Smartphones, which are made available by the global population, exploit the Internet connection to report a signal to a central server every time a vibration is detected by the on-board accelerometer sensor. This paper introduces a statistical approach for the detection of earthquakes from the data coming from the network of smartphones. The approach allows to handle a dynamic network in which the number of active nodes constantly changes and where nodes are heterogeneous in terms of sensor sensibility and transmission delay. Additionally, the approach allows to keep the probability of false alarm under control. The statistical approach is applied to the data collected by three subnetworks related to the cities of Santiago (Chile), Iquique (Chile) and Kathmandu (Nepal). The detection capabilities of the approach are discussed in terms of earthquake magnitude and detection delay. A simulation study is carried out in order to link the probability of detection and the detection delay to the behaviour of the network under an earthquake event.
Similar content being viewed by others
References
Basseville M, Nikiforov IV et al (1993) Detection of abrupt changes: theory and application, vol 104. Prentice Hall, Englewood Cliffs
Borror CM, Champ CW, Rigdon SE (1998) Poisson EWMA control charts. J Qual Technol 30(4):352–361
Brenning A, Dubois G (2007) Towards generic real-time mapping algorithms for environmental monitoring and emergency detection. Stoch Environ Res Risk Assess 22(5):601–611
Capizzi G, Masarotto G (2008) Practical design of generalized likelihood ratio control charts for autocorrelated data. Technometrics 50(3):357–370
Chetouani Y (2007) A neural network approach for the real-time detection of faults. Stoch Environ Res Risk Assess 22(3):339–349
Clayton RW, Heaton T, Chandy M, Krause A, Kohler M, Bunn J, Guy R, Olson M, Faulkner M, Cheng M et al (2012) Community seismic network. Ann Geophys 54(6):738–747
Cochran ES, Lawrence JF, Christensen C, Jakka RS (2009) The quake-catcher network: citizen science expanding seismic horizons. Seismol Res Lett 80(1):26–30
Cochran ES, Lawrence JF, Kaiser A, Fry B, Chung A, Christensen C (2012) Comparison between low-cost and traditional MEMS accelerometers: a case study from the M7.1 Darfield, New Zealand, aftershock deployment. Ann Geophys 54(6):728–737
Cua G, Fischer M, Heaton T, Wiemer S (2009) Real-time performance of the virtual seismologist earthquake early warning algorithm in southern California. Seismol Res Lett 80(5):740–747
D’Alessandro A, D’Anna G (2013) Suitability of low-cost three-axis MEMS accelerometers in strong-motion seismology: tests on the LIS331DLH (iPhone) accelerometer. Bull Seismol Soc Am 103(5):2906–2913
Gasparini P, Manfredi G, Zschau J (2007) Earthquake early warning systems. Springer, New York
Given DD, Cochran ES, Heaton T, Hauksson E, Allen R, Hellweg P, Vidale J, Bodin P (2014) Technical implementation plan for the ShakeAlert production system: an earthquake early warning system for the west coast of the united states. Technical report, US Geological Survey
He B, Xie M, Goh T, Tsui K (2006) On control charts based on the generalized Poisson model. Qual Technol Quant Manag 3(4):383–400
Lancieri M, Zollo A (2008) A bayesian approach to the real-time estimation of magnitude from the early P and S wave displacement peaks. J Geophys Res 113(B12):B12302
Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT (2010) A survey of mobile phone sensing. Commun Mag, IEEE 48(9):140–150
Mei Y, Han SW, Tsui KL (2011) Early detection of a change in Poisson rate after accounting for population size effects. Stat Sin 21(2):597
Minson SE, Brooks BA, Glennie CL, Murray JR, Langbein JO, Owen SE, Heaton TH, Iannucci RA, Hauser DL (2015) Crowdsourced earthquake early warning. Sci Adv 1(3):e1500036. doi:10.1126/sciadv.1500036
Overeem A, R Robinson JC, Leijnse H, Steeneveld GJ, P Horn B, Uijlenhoet R (2013) Crowdsourcing urban air temperatures from smartphone battery temperatures. Geophys Res Lett 40(15):4081–4085
Satriano C, Wu YM, Zollo A, Kanamori H (2011) Earthquake early warning: concepts, methods and physical grounds. Soil Dyn Earthq Eng 31(2):106–118
Snik F, Rietjens JH, Apituley A, Volten H, Mijling B, Di Noia A, Heikamp S, Heinsbroek RC, Hasekamp OP, Smit JM et al (2014) Mapping atmospheric aerosols with a citizen science network of smartphone spectropolarimeters. Geophys Res Lett 41(20):7351–7358
Snyder DL, Miller MI (2012) Random point processes in time and space. Springer Science & Business Media, New York
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Finazzi, F., Fassò, A. A statistical approach to crowdsourced smartphone-based earthquake early warning systems. Stoch Environ Res Risk Assess 31, 1649–1658 (2017). https://doi.org/10.1007/s00477-016-1240-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00477-016-1240-8