ESC 2012 Moscow Seismic Risk Assessment for Earthquake Early Warning - - PowerPoint PPT Presentation

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ESC 2012 Moscow Seismic Risk Assessment for Earthquake Early Warning - - PowerPoint PPT Presentation

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ESC 2012 Moscow Seismic Risk Assessment for Earthquake Early Warning and Rapid Response Systems: the Bishkek (Kyrgyzstan) test case Massimiliano Pittore , D. Bindi, K. Fleming, S. Parolai, M. Picozzi, M. Pilz, J. Stankiewicz, J. Tyagunov, S.

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ESC 2012 Moscow Seismic Risk Assessment for Earthquake Early Warning and Rapid Response Systems: the Bishkek (Kyrgyzstan) test case

Massimiliano Pittore,

  • D. Bindi, K. Fleming, S. Parolai, M. Picozzi, M. Pilz, J. Stankiewicz,
  • J. Tyagunov, S. Ullah, M. Wieland, J. Zschau

GFZ Potsdam, Sect. 2.1 - Seismic Risk and Early Warning, GFZ Potsdam - Centre for Early Warning

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Pittore et Al. ESC 2012 Moscow

  • Earthquake Early Warning (EEW) systems in Central Asia
  • Overall design
  • Risk Assessment for EEW Systems, test case: Bishkek
  • Scenario design and earthquake simulation
  • Risk Assessment for considered scenario
  • Conclusions

Summary

August 21th

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Pittore et Al. ESC 2012 Moscow

Distribution of expected casualties – hazard with exc. prob. 10% in 50 yrs

Earthquake Early Warning (EEW) systems in Central Asia

August 21th

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Distribution of expected casualties – hazard with exc. prob. 10% in 50 yrs Desired Requirements

  • two use-scenarios:

regional - pre-event measures, risk mitigation local - search & rescue optimization, disaster management

  • target-driven, focused approach
  • optimization of sensors network
  • fast, reliable event characterization
  • spatially detailed, uncertainty-aware risk assesment, with

efficient management of (lack of) information Desired Requirements

  • two use-scenarios:

regional - pre-event measures, risk mitigation local - search & rescue optimization, disaster management

  • target-driven, focused approach
  • optimization of sensors network
  • fast, reliable event characterization
  • spatially detailed, uncertainty-aware risk assesment, with

efficient management of (lack of) information

Earthquake Early Warning (EEW) systems in Central Asia

August 21th

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Optimized placement of e.w. seismic stations (e.g. Bishkek - Almaty) Intensity scenario at target site (e.g. Bishkek) Real-time Damage/Loss Map (e.g. Bishkek) Pittore et Al. ESC 2012 Moscow

EEW Systems in Central Asia: proposed design

August 21th

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Evolutionary Event Characterization

Lead Time: 22 sec

Real-time filtering of damage and loss scenarios

Lead Time: 17 sec Lead Time: 14 sec

Event characterized, most probable scenario selected. Broadcast warning

time August 21th

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Regional Network Optimization

Almaty Bishkek Location of 1911 Mw7.7 Kemin Earthquake August 21th

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Pittore et Al. ESC 2012 Moscow

Multi-source, multi-scale exposure and vulnerability assessment

Stratification based on Analysis of MR satellite images

Urban Structure Type: 10 Type: 3-6 storey brick, concrete, panel Age: built before 1977

Bishkek August 21th

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Pittore et Al. ESC 2012 Moscow

Multi-source, multi-scale exposure and vulnerability assessment

Ground-based sampling based on Rapid Visual Screening (RVS) and Omnidirectional Imaging Bishkek Stratified sampling August 21th

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conditional probability table (V)

Bayesian networks

posterior probability A B C D E F

background image: earth.google.com

Age: 1994-2009 Height: 29 m

  • No. of storeys: 9

WHE Type: 6

  • Vuln. (EMS-98): E

Probabilistic data integration for Vulnerability and Risk assessment

August 21th

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Pittore et Al. ESC 2012 Moscow

Bayesian networks

Probabilistic data integration for Vulnerability and Risk assessment

I

Intensity

D

Damage

EMS-98 Damage posterior probability

D0 D1 D2 D3 D4 D5

August 21th

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Earthquake Scenario considered test case

Bishkek

  • Strike: East-west dip=50o

reverse mechanism

  • Two scenarios, M=7 and M=7.5 with

stress drop varying from 2 to 200 bars. Issyk-Ata faults system Modelled fault August 21th

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Stochastic simulation using EXSIM (Motazedian and Atkinson 2005). Point-source contributions from each sub-fault are summed at

  • bservation sites with proper time delays.
  • >

Random noise

time

Deterministic envelope

time

=

time

X

frequency

=

frequency frequency

Point-source-like reference spectrum

Earthquake Scenario Simulation Scheme

August 21th

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Empirical estimates of site effects available at 19 sites (Parolai et al 2010) are convolved with simulated spectra and transformed to MSK-intensity following the study

  • f Sokolok and Chernov (1998) on the correlation between Fourier amplitude spectra
  • f acceleration and intensity. For each site, a distribution of intensities is

computed (related to the variability of stress drop introduced in the simulations) Frequency [Hz] Spectral ratio

Earthquake Scenario Site Effects Correction

August 21th

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Earthquake Scenario M7 Spatial Distribution of Simulated Intensity

August 21th

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Mean Vulnerability Index (MVI)

  • Est. nr.
  • f

building s Est. populatio n 0.45-0.50 25582 99969 0.50-0.55 15722 266175 0.55-0.60 34377 227410 0.60-065 24322 140810 0.65-0.70 6606 110130 0.70-0.75 4177 0.75-0.80 1507 3145 TOTAL 112293 847639

MVI = 1 (n−1)( ∑

i =0...n−1

p(V i)(n−i)−1)

Bishkek - Vulnerability Model

Building spatial densities have been Estimated by fitting a 2D Poisson Point Process to a training set of building footprints August 21th

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Earthquake Scenario Macroseismic Intensity vs. Building Density

Spatial density of buidings exposed to MSK I ≥ 6 Spatial density of buidings exposed to MSK I ≥ 7 Spatial density of buidings exposed to MSK I ≥ 8

Building distributions estimated by averaging stochastic realizations of 2D Poisson Point Process in any geocell August 21th

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Earthquake Scenario Damage Probability of Exceedance

August 21th

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Earthquake Scenario Expected Spatial Density of Collapses

August 21th

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  • Evolutionary Event Characterization and network optimization show a

great potential in application to Earthquake Early Warning (EEW) Systems.

  • Next´s generation EEW Systems need reliable, spatially detailed and

up-to-date Risk Assessment.

  • Several Risk Scenarios for Bishkek are under assessment, with

uncertainty modelling and high spatial disaggregation. Preliminary results are very encouraging.

  • Careful data collection and integration and new technologies will be

further explored in a multiple-scale, holistic framework.

Conclusions

August 21th

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Thank you! Спасибо!

August 21th

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August 21th Generate a number

  • f random networks

Eveluate the networks by computing lead times for scenario earthquakes Select the best available networks Create new generation of networks by combining elements of the best ones Evaluate new generation of networks. Improvement? yes

  • No. Any improvement over

last 10 generations?

  • Yes. Perform some

random mutations Algorithm Converged. Optimal Network found No

EEW network optmization Via Genetic Algorithm