Envelope-based Seismic Early Warning: Virtual Seismologist method G. Cua and T. Heaton Caltech
Outline Virtual Seismologist method Bayes’ Theorem Ratios of ground motion as magnitude indicators Examples of useful prior information
Virtual Seismologist method for seismic early warning Bayesian approach to seismic early warning designed for regions with distributed seismic hazard/risk modeled on “back of the envelope” methods of human seismologists for examining waveform data Shape of envelopes, relative frequency content Capacity to assimilate different types of information Previously observed seismicity state of health of seismic network site amplification
Given available waveform observations Y obs, what are the most probable estimates of magnitude and location, M, R? “likelihood”“prior” “posterior” prior = beliefs regarding M, R without considering waveform data, Y obs likelihood = how waveform observations Y obs modify our beliefs posterior = current state of belief, a combination of prior beliefs,Y obs maxima of posterior = most probable estimates of M, R given Y obs spread of posterior = variance on estimates Bayes’ Theorem: a review “the answer”
HEC 36.7 km DAN 81.8 km PLC 88.2 km VTV 97.2 km Example: 16 Oct 1999 Mw7.1 Hector Mine Maximum envelope amplitudes at HEC, 5 seconds After P arrival
Defining the likelihood (1): attenuation relationships maximum velocity 5 sec. after P-wave arrival at HEC prob(Y vel =1.0cm/s | M, R) xxx
Estimating magnitude from ground motion ratios Slope= Int = 7.88 P-wave frequency content scales with magnitude (Allen & Kanamori, Nakamura) linear discriminant analysis on acceleration and displacement M = -0.3 log(Acc) log(Disp) M 5 sec after HEC = 6.1 P-wave
from P-wave velocity Estimating M, R from waveform data: 5 sec after P-wave arrival at HEC “best” estimate of M, R 5 seconds after P-wave arrival using acceleration, velocity, displacement M 5 sec after HEC = 6.1 P-wave from P-wave acceleration, displacement Magnitude Distance Magnitude Distance
Examples of Prior Information 1) Gutenberg-Richter log(N)=a-bM 2)voronoi cells- nearest neighbor regions for all operating stations Pr ( R ) ~ R 3) previously observed seismicity STEP (Gerstenberger et al, 2003), ETAS (Helmstetter, 2003) foreshock/aftershock statistics (Jones, 1985) “poor man” version – increase probability of location by small % relative to background
Voronoi & seismicity prior M, R estimate from waveform data peak acc,vel,disp 5 sec after P arrival at HEC M 5 sec =6.1 M, location estimate combining waveform data & prior ~5 km
A Bayesian framework for real-time seismology Predicting ground motions at particular sites in real-time Cost-effective decisions using data available at a given time Acceleration Amplification Relative to Average Rock Station
Conclusions Bayes’ Theorem is a powerful framework for real- time seismology Source estimation in seismic early warning Predicting ground motions Automating decisions based on real-time source estimates formalizing common sense Ratios of ground motion can be used as indicators of magntiude Short-term earthquake forecasts, such as ETAS (Helmsetter) and STEP (Gerstenberger et al) are good candidate priors for seismic early warning
Linear discriminant analysis groups by magnitude Ratio of among group to within group covariance is maximized by: Z= 0.27 log(Acc) – 0.96 log(Disp) Lower bound on Magnitude as a function of Z: M low = Z = -0.3 log(Acc) log(Disp) Slope= Int = 7.88 Defining the likelihood (2): ground motion ratios M low(HEC) = -0.3 log(65 cm/s/s) log(6.89e-2 cm) = 6.1
Other groups working on this problem Kanamori, Allen and Kanamori – Southern California Espinoza-Aranda et al – Mexico City Wenzel et al – Bucharest, Istanbul Nakamura – UREDAS (Japan Railway) Japan Meteorological Agency – NOWCAST Leach and Dowla – nuclear plants Central Weather Bureau, Taiwan
Q1: Given available data, what is most probable magnitude and location estimate? Q2: Given a magnitude and location estimate, what are the expected ground motions? Seismic Early Warning