Rapid and reliable seismic source characterization in earthquake early warning systems: current methodologies, results, and new perspectives

In the present paper, we provided a review of the main principles and methodologies on which the current earthquake early warning systems are grounded and will also provide a perspective view for next future developments and improvements. First, we introduce the standard methodologies for the source characterization in earthquake early warning, with a special focus on the real-time earthquake magnitude determination. We discuss the suitability of existent methodologies and empirical regression laws for very large events. We then present the different approaches for the rapid prediction of the ground shaking and of the potential damaged zone, both based on traditional seismic data and on the use of continuous GPS data. Finally, the last part of the paper provides the perspective view toward a next generation of early warning systems, linking new research achievements about the earthquake rupture nucleation and the development of new methods/technologies aimed at a fast and high-resolution, real-time modeling of the ongoing source process and accurate prediction of the quake shaking at the regional and local scale.

[1]  Yih-Min Wu,et al.  Development of an Earthquake Early Warning System Using Real-Time Strong Motion Signals , 2008, Sensors.

[2]  J Jastrow.  PSYCHOLOGICAL NOTES UPON SLEIGHT-OF-HAND EXPERTS. , 1896, Science.

[3]  S. Colombelli,et al.  Evidence for a difference in rupture initiation between small and large earthquakes , 2014, Nature Communications.

[4]  Yuji Yagi,et al.  A unified source model for the 2011 Tohoku earthquake , 2011 .

[5]  Tomowo Hirasawa,et al.  Body wave spectra from propagating shear cracks. , 1973 .

[6]  Jean-Paul Ampuero,et al.  Self‐similar slip pulses during rate‐and‐state earthquake nucleation , 2009 .

[7]  Yusaku Ohta,et al.  Quasi real‐time fault model estimation for near‐field tsunami forecasting based on RTK‐GPS analysis: Application to the 2011 Tohoku‐Oki earthquake (Mw 9.0) , 2012 .

[8]  Teruo Yamashita,et al.  A cohesive zone model for dynamic shear faulting based on experimentally inferred constitutive relation and strong motion source parameters , 1989 .

[9]  D. Melgar,et al.  Real‐time inversion of GPS data for finite fault modeling and rapid hazard assessment , 2012 .

[10]  E. Hauksson,et al.  Technical implementation plan for the ShakeAlert production system: an Earthquake Early Warning system for the West Coast of the United States , 2014 .

[11]  Michel Campillo,et al.  Fault finiteness and initiation of dynamic shear instability , 2000 .

[12]  Dino Bindi,et al.  Earthquake scaling characteristics and the scale‐(in)dependence of seismic energy‐to‐moment ratio: Insights from KiK‐net data in Japan , 2010 .

[13]  Aldo Zollo,et al.  Exploring the feasibility of a nationwide earthquake early warning system in Italy , 2015 .

[14]  Jascha Polet,et al.  Rapid calculation of a Centroid Moment Tensor and waveheight predictions around the north Pacific for the 2011 off the Pacific coast of Tohoku Earthquake , 2011 .

[15]  John R. Rice,et al.  Universal nucleation length for slip-weakening rupture instability under nonuniform fault loading , 2003 .

[16]  Jean-Paul Ampuero,et al.  A mechanism for preseismic steady rupture fronts observed in laboratory experiments , 2011 .

[17]  Li Zhao,et al.  Magnitude estimation using the first three seconds P‐wave amplitude in earthquake early warning , 2006 .

[18]  Gaetano Festa,et al.  Early magnitude and potential damage zone estimates for the great Mw 9 Tohoku‐Oki earthquake , 2012 .

[19]  Hiroo Kanamori,et al.  Real-Time Seismology and Earthquake Damage Mitigation , 2005 .

[20]  Elisa Tinti,et al.  Modeling the dynamic rupture propagation on heterogeneous faults with rate- and state-dependent friction , 2005 .

[21]  H. Kanamori,et al.  The Potential for Earthquake Early Warning in Southern California , 2003, Science.

[22]  Hiroo Kanamori,et al.  A threshold-based earthquake early warning using dense accelerometer networks , 2010 .

[23]  Jean-Paul Ampuero,et al.  A window into the complexity of the dynamic rupture of the 2011 Mw 9 Tohoku‐Oki earthquake , 2011 .

[24]  Maorong Ge,et al.  The 2011 Mw 9.0 Tohoku Earthquake: Comparison of GPS and Strong‐Motion Data , 2013 .

[25]  Hiroo Kanamori,et al.  Experiment on an Onsite Early Warning Method for the Taiwan Early Warning System , 2005 .

[26]  Jacopo Taddeucci,et al.  Experimental observation of stick‐slip instability fronts , 2010 .

[27]  W. Ellsworth,et al.  Seismic Evidence for an Earthquake Nucleation Phase , 1995, Science.

[28]  P. Bernard,et al.  High-frequency seismic radiation from a buried circular fault , 1984 .

[29]  Stefano Parolai,et al.  Automatic imaging of earthquake rupture processes by iterative deconvolution and stacking of high‐rate GPS and strong motion seismograms , 2014 .

[30]  Aldo Zollo,et al.  Reply to comment by P. Rydelek et al. on “Earthquake magnitude estimation from peak amplitudes of very early seismic signals on strong motion records” , 2007 .

[31]  Marcus Y. Woo,et al.  Predicting the Unpredictable: The Tumultuous Science of Earthquake Prediction , 2010 .

[32]  Stefan Wiemer,et al.  Preface to the special issue “Strategic applications of real-time risk mitigation strategies and tools: case studies and lessons learned in REAKT” , 2016, Bulletin of Earthquake Engineering.

[33]  John A. Hammer,et al.  The cargo-binding domain regulates structure and activity of myosin 5 , 2006, Nature.

[34]  Richard M. Allen,et al.  Application of real‐time GPS to earthquake early warning in subduction and strike‐slip environments , 2013 .

[35]  Richard M. Allen,et al.  Application of real‐time GPS to earthquake early warning , 2011 .

[36]  P. Rydelek,et al.  Earth science: Is earthquake rupture deterministic? , 2006, Nature.

[37]  Mitsuyuki Hoshiba,et al.  Outline of the 2011 off the Pacific coast of Tohoku Earthquake (Mw 9.0) —Earthquake Early Warning and observed seismic intensity— , 2011 .

[38]  Aldo Zollo,et al.  A Bayesian approach to the real-time estimation of magnitude from the early P and S wave displacement peaks , 2008 .

[39]  Hiroo Kanamori,et al.  Test of a Threshold‐Based Earthquake Early‐Warning Method Using Japanese Data , 2012 .

[40]  Keiiti Aki,et al.  Nonuniformity of the constitutive law parameters for shear rupture and quasistatic nucleation to dynamic rupture: a physical model of earthquake generation processes. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[41]  Mitiyasu Ohnaka,et al.  A Physical Scaling Relation Between the Size of an Earthquake and its Nucleation Zone Size , 2000 .

[42]  Gaetano Festa,et al.  Twin ruptures grew to build up the giant 2011 Tohoku, Japan, earthquake , 2012, Scientific Reports.

[43]  Aldo Zollo,et al.  A P wave‐based, on‐site method for earthquake early warning , 2015 .

[44]  Aldo Zollo,et al.  Earthquake magnitude estimation from peak amplitudes of very early seismic signals on strong motion records , 2006 .

[45]  T. Wright,et al.  Real‐time, reliable magnitudes for large earthquakes from 1 Hz GPS precise point positioning: The 2011 Tohoku‐Oki (Japan) earthquake , 2012 .

[46]  Jean-Pierre Vilotte,et al.  Nucleation of rupture under slip dependent friction law: Simple models of fault zone , 2002 .

[47]  K. Aki,et al.  Quantitative Seismology, 2nd Ed. , 2002 .

[48]  Aldo Zollo,et al.  Fast determination of earthquake magnitude and fault extent from real-time P-wave recordings , 2015 .

[49]  Gregory C. Beroza,et al.  Properties of the seismic nucleation phase , 1996 .

[50]  Gaetano Festa,et al.  Earthquake magnitude estimation from early radiated energy , 2008 .

[51]  Changjiang Wu,et al.  Comment on “Earthquake magnitude estimation from peak amplitudes of very early seismic signals on strong motion records” by Aldo Zollo, Maria Lancieri, and Stefan Nielsen , 2007 .

[52]  Friedemann Wenzel,et al.  Earthquake early warning for Bucharest, Romania: Novel and revised scaling relations , 2007 .