1 / 16

Envelope-based Seismic Early Warning: Virtual Seismologist method

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.

ayla
Télécharger la présentation

Envelope-based Seismic Early Warning: Virtual Seismologist method

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Envelope-based Seismic Early Warning: Virtual Seismologist method G. Cua and T. Heaton Caltech

  2. Outline • Virtual Seismologist method • Bayes’ Theorem • Ratios of ground motion as magnitude indicators • Examples of useful prior information

  3. 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

  4. Bayes’ Theorem: a review Given available waveform observations Yobs , what are the most probable estimates of magnitude and location, M, R? “posterior” “likelihood” “prior” “the answer” • prior = beliefs regarding M, R without considering waveform data, Yobs • likelihood = how waveform observations Yobs modify our beliefs • posterior= current state of belief, a combination of prior beliefs,Yobs • maxima of posterior = most probable estimates of M, R given Yobs • spread of posterior = variance on estimates

  5. Example: 16 Oct 1999 Mw7.1 Hector Mine HEC 36.7 km DAN 81.8 km PLC 88.2 km VTV 97.2 km Maximum envelope amplitudes at HEC, 5 seconds After P arrival

  6. Defining the likelihood (1): attenuation relationships maximum velocity 5 sec. after P-wave arrival at HEC x x x prob(Yvel=1.0cm/s | M, R)

  7. Estimating magnitude from ground motion ratios • P-wave frequency content scales with magnitude (Allen & Kanamori, • Nakamura) • linear discriminant analysis on acceleration and displacement Slope=-1.114 Int = 7.88 M= -0.3 log(Acc) + 1.07 log(Disp) + 7.88 M 5 sec after HEC = 6.1 P-wave

  8. Estimating M, R from waveform data:5 sec after P-wave arrival at HEC from P-wave velocity “best” estimate of M, R 5 seconds after P-wave arrival using acceleration, velocity, displacement Distance Distance Magnitude Magnitude M 5 sec after HEC = 6.1 P-wave from P-wave acceleration, displacement

  9. Examples of Prior Information • Gutenberg-Richter log(N)=a-bM • voronoi cells- nearest neighbor regions for all operating stations • Pr ( R ) ~ R • 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

  10. M, location estimate combining waveform data & prior Voronoi & seismicity prior M5 sec=6.1 M, R estimate from waveform data peak acc,vel,disp 5 sec after P arrival at HEC ~5 km

  11. 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

  12. 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

  13. Defining the likelihood (2): ground motion ratios • 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 onMagnitude as a function of Z: Mlow = -1.114 Z + 7.88 = -0.3 log(Acc) + 1.07 log(Disp) + 7.88 Slope=-1.114 Int = 7.88 Mlow(HEC) = -0.3 log(65 cm/s/s) + 1.07 log(6.89e-2 cm) + 7.88 = 6.1

  14. 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

  15. Seismic Early Warning 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?

More Related