Real-Time Estimation of Fault Rupture Extent Using Near-Source versus Far-Source Classification

To estimate the fault dimension of an earthquake in real time, we present a methodology to classify seismic records into near-source or far-source records. Characteristics of ground motion, such as peak ground acceleration, have a strong correlation with the distance from a fault rupture for large earthquakes. This study analyzes peak ground motions and finds the function that best classifies near-source and far-source records based on these parameters. We perform (1) Fisher’s linear discriminant analysis and two different Bayesian methods to find the coefficients of the linear discriminant function and (2) Bayesian model class selection to find the best combination of the peak ground-motion parameters. Bayesian model class selection shows that the combination of vertical acceleration and horizontal velocity produces the best performance for the classification. The linear discriminant function produced by the three methods classifies near-source and far-source data, and in addition, the Bayesian methods give the probability for a station to be near-source, based on the ground-motion measurements. This discriminant function is useful to estimate the fault rupture dimension in real time, especially for large earthquakes.

[1]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[2]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[3]  Thomas C. Hanks,et al.  Geophysical assessment of peak accelerations , 1976 .

[4]  Robin K. McGuire,et al.  The character of high-frequency strong ground motion , 1981 .

[5]  David M. Boore,et al.  Peak horizontal acceleration and velocity from strong motion records including records from the 1979 Imperial Valley, California, earthquake , 1981 .

[6]  Kenneth W. Campbell,et al.  Near-source attenuation of peak horizontal acceleration , 1981 .

[7]  Thomas H. Heaton,et al.  Inversion of strong ground motion and teleseismic waveform data for the fault rupture history of the 1979 Imperial Valley, California, earthquake , 1983 .

[8]  Chia-Yen Peng,et al.  Strong-motion earthquake measurement using a digital accelerograph , 1984 .

[9]  S. Gull.  Bayesian Inductive Inference and Maximum Entropy , 1988 .

[10]  Thomas H. Heaton,et al.  Rupture model of the 1989 Loma Prieta earthquake from the inversion of strong-motion and broadband teleseismic data , 1991, Bulletin of the Seismological Society of America.

[11]  Walter H. F. Smith,et al.  Free software helps map and display data , 1991 .

[12]  D. Wald,et al.  Spatial and temporal distribution of slip for the 1992 Landers, California, earthquake , 1994, Bulletin of the Seismological Society of America.

[13]  Takao Kagawa,et al.  STRONG MOTION RECORDS IN THE SOURCE AREA OF THE HYOGOKEN-NAMBU EARTHQUAKE, JANUARY 17,1995,JAPAN , 1995 .

[14]  Costas Papadimitriou,et al.  Asymptotic Expansions for Reliabilities and Moments of Uncertain Dynamic Systems , 1995 .

[15]  Thomas H. Heaton,et al.  The slip history of the 1994 Northridge, California, earthquake determined from strong-motion, teleseismic, GPS, and leveling data , 1996, Bulletin of the Seismological Society of America.

[16]  Kojiro Irikura,et al.  Minute Locating of Faulting beneath Kobe and the Waveform Inversion of the Source Process during the 1995 Hyogo-ken Nanbu, Japan, Earthquake Using Strong Ground Motion Records , 1996 .

[17]  D. Wald.  Slip History of the 1995 Kobe, Japan, Earthquake Determined from Strong Motion, Teleseismic, and Geodetic Data , 1996 .

[18]  John Skilling,et al.  Data analysis : a Bayesian tutorial , 1996 .

[19]  J. Beck,et al.  Asymptotic Expansions for Reliability and Moments of Uncertain Systems , 1997 .

[20]  A. Gelman,et al.  Weak convergence and optimal scaling of random walk Metropolis algorithms , 1997 .

[21]  David J. C. Mackay,et al.  Introduction to Monte Carlo Methods , 1998, Learning in Graphical Models.

[22]  J. Beck,et al.  Updating Models and Their Uncertainties. I: Bayesian Statistical Framework , 1998 .

[23]  Yi Li,et al.  Bayesian automatic relevance determination algorithms for classifying gene expression data. , 2002, Bioinformatics.

[24]  T. Iwata,et al.  Rupture Process of the 1999 Kocaeli, Turkey, Earthquake Estimated from Strong-Motion Waveforms , 2002 .

[25]  Polat Gülkan,et al.  A Critical Examination of Near-Field Accelerograms from the Sea of Marmara Region Earthquakes , 2002 .

[26]  Brian D. Ripley,et al.  Modern Applied Statistics with S Fourth edition , 2002 .

[27]  Shinji Sato,et al.  A New Method of Quickly Estimating Epicentral Distance and Magnitude from a Single Seismic Record , 2003 .

[28]  Chen Ji,et al.  Broadband modeling of the 2002 Denali fault earthquake on the Earth Simulator , 2003 .

[29]  C. Ji,et al.  Slip history and dynamic implications of the 1999 Chi‐Chi, Taiwan, earthquake , 2003 .

[30]  E. Jaynes.  Probability theory : the logic of science , 2003 .

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

[32]  D. Boore.  Effect of Baseline Corrections on Displacements and Response Spectra for Several Recordings of the 1999 Chi-Chi, Taiwan, Earthquake , 2004 .

[33]  Tzay-Chyn Shin,et al.  CWB Free-Field Strong-Motion Data from the 21 September Chi-Chi, Taiwan, Earthquake , 2004 .

[34]  R. Baierlein.  Probability Theory: The Logic of Science , 2004 .

[35]  J. Beck,et al.  Model Selection using Response Measurements: Bayesian Probabilistic Approach , 2004 .

[36]  Tzay-Chyn Shin,et al.  An Overview of the 1999 Chi-Chi, Taiwan, Earthquake , 2004 .

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

[38]  S. Aoi,et al.  Ground motion and rupture process of the 2004 Mid Niigata Prefecture earthquake obtained from strong motion data of K-NET and KiK-net , 2005 .

[39]  P. Mahadevan,et al.  An overview , 2007, Journal of Biosciences.

[40]  James L. Beck,et al.  Bayesian Updating and Model Class Selection for Hysteretic Structural Models Using Stochastic Simulation , 2008 .