Towards a quantitative performance evaluation of VS and Elarms

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1 Towards a quantitative performance evaluation of VS and Elarms M.Caprio,G. Cua, S. Wiemer, M. Fischer 10/4/07

2 Outlook Introduction to Virtual Seismologist Applications Results Comparison criteria between VS and Elarms Future works

3 Introduction to Virtual Seismologist developped by Cua & Heaton 2005 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 Known fault locations Gutenberg-Richter recurrence relationship

4 Given available waveform observations Yobs, what are the most probable estimates of magnitude and location, M, R? posterior (the answer) likelihood prior prob(m, R Y obs ) prob(y obs M, R) prob(m, R) Prior = beliefs regarding M, R before considering observations Yobs Likelihood = how observations Yobs modify beliefs about M, R Posterior = current state of belief, combination of prior and Yobs maxima of posterior = most probable estimates of M, R given Yobs Introduction to Virtual Seismologist spread of posterior = variances on estimates of M, R

5 Calibration of VS to Switzerland log10 A = am + b(r1 + C(M )) + d log10 (R1 + C(M )) + e R1 = R + 9 PGA (S-wave acceleration) M7.5 M4.5 M2.5 10/4/07

CISN Early Warning Project provided by Richard Allen Goal: California-wide evaluation of real-time early warning methodologies Aug 06 Year 1: Initial real-time outputs from EEW algorithms Year 2:

6 CISN Early Warning Project provided by Richard Allen Goal: California-wide evaluation of real-time early warning methodologies Aug 06 Year 1: Initial real-time outputs from EEW algorithms Year 2: Adding uncertainty estimates; website display Currently at 21 mths Year 3: Evaluation of past and future performance Jul 09 specifications for implemented early warning system equipment requirements (stations) telemetry processing

7 Three real-time algorithms in implementation Onsite warning (Wu and Kanamori, Caltech) Single waveform approach to warning maximizes warning time Developed station specific sensitivity parameters to optimize performance Virtual Seismologist (Cua and Heaton, ETH) Bayesian approach to source (magnitude and location) and ground motion estimation, and framework for optimized user-response to uncertain information On-going research on hybrid weak + strong motion level attenuation relationships (Cua and Heaton), real-time characterization of source finiteness (Yamada and Heaton) 3. ElarmS (Allen and Kanamori, UC Berkeley) Use of amplitude and period information in magnitude estimate Integration of ShakeMap components for output

8 Block diagram of the CISN Early Warning Framework codes Common block analyzing wave forms for all different EEW algorithm Algorithms run all together on the same data set

9 Block diagram of VS codes as installed in Switzerland The VS code require the same input as in the CISN framework codes and the RT interface will be built from ETH early warning group Location module is under development in case of less than 4 recording station

Application in Switzerland 6 7 SAUR SBAF SBAP SBAT SBIS BOURR 8 SKAF SMZW SRHB OTTER BALST SLE TRULL ACB SULZ FLACH ZUR 9 STEIN WILA WEIN LIENZ 10 DAVA 11 WTTA 12 47 46 45 MUO PLONS BRANT SCUC HASLI

10 Application in Switzerland 6 7 SAUR SBAF SBAP SBAT SBIS BOURR 8 SKAF SMZW SRHB OTTER BALST SLE TRULL ACB SULZ FLACH ZUR 9 STEIN WILA WEIN LIENZ 10 DAVA 11 WTTA MUO PLONS BRANT SCUC HASLI BNALP DAVOX LLS TORNY SZER STSP LUKA1 GIMEL WIMIS RITOM DOETR VDL FUORN MOSI SENIN NARA LKBD AIGLE FUSIO TUE BERNI LKBD2 CHIR2 SMUK SIOO CHAT1 SMUR GRYON CHAT2 SIOV MFSFA SALAN MFSFB EMV DIX TONGO MMK MABI MUGIO MRGE MDI BNI RSP MONC BOB SALO BOSI km ROSI ABSI KOSI Shape: sensor type broadband velocity accelerometer 5sec velocity 1sec velocity 12bit accelerometer Colour: network SDSnet AlpTransit INGV Italy Bovio Italy ZAMG Austria SMSnet RISI DOI

11 Application of VS in Switzerland 8 December 2006 Basel earthquake Ml= N, 7.6E, depth=5 km VS evaluations M20sec=3.2 M25sec=3.3 M40sec=3.2 loc20sec=15km loc25sec=15km loc40sec=16km M seconds location error (km) ! seconds

12 Application in Southern California

13 VS evaluations M10sec=4.5 M15sec=4.7 M30sec=4.9 loc10sec=2km loc5sec=2km loc30sec=1km 8 Application in Southern California eventid /09/02, 17:29: Ml= !120!119!118!117!116! M seconds location error (km) ! seconds

14 Application in Southern California VS evaluations M10sec=4.5 M15sec=4.7 M30sec=4.9 loc10sec=2km loc5sec=2km loc30sec=1km number_of_triggers location_error_km origin_time_error_sec ElarmS: Event Summary sca lat lon dp Mag 4.68 ElarmS evaluations M10sec=4.7 M15sec=4.7 M30sec=4.7 loc10sec=2km loc5sec=2km loc30sec2km 7 mag_estimate mag_estimate_error number_of_pt_amp_obs Jun 23 10:07:36 time_after_event_origin_time plotev.summary /home/projects/elarms/elarms/marta/ /process

15 Event-based performance evaluation Challenges: Comparing algorithms with different types of output ElarmS and VS produce magnitude and location, and peak ground motion estimates for a given region; these are updated every second. Other existing methods... Summarizing VS and ElarmS outputs VS and Elarms estimate ground motions at a given set of stations, and these are updated every second. Thus, for a southern California event, considering 130+ real-time stations, the typical output of these regional approaches is a set of 260+ time 60-second long time series of predicted PGA and PGV at each station

16 Quality criteria proposed by CISN Advantages Express ground motion predictions and prediction errors in MMI Non-experts are more familiar with MMI than absolute levels of PGA or PGV Public is already familiar with the ShakeMap color scale, shaking level and Damage descriptions

17 Quality criteria proposed by CISN Wald (1999) relationships between PGA/PGV and MMI I = 2.10log(PGV ) for I < V = 3.47 log(pgv ) for I > V Hauksson & Böse (2008) ΔI = I est I obs = 2.10 log PGV est PGV obs PGV obs for I < V = 3.47 log PGV est for I > V GM prediction error in units of intensity GM prediction error scale abs(δi) 0.5 "Very good" 0.5 < abs(δi) 1.0 "Good" 1.0 < abs(δi) 1.5 "Moderate" 1.5 < abs(δi) 2.0 "Poor" abs(δi) > 2.0 "Extremely poor" 1 unit intensity is approximately 1 sigma in attenuation (factor 2 in ground motion)

18 Magnitude : 4.57 Origin Time : 2007/08/09 07:58:49 Lat : Lon : Z : 7.58 Example for On-site approach provided by Hauksson & Böse Station time PGVobs PGVest abs(δi) observed GM predicted GM prediction performance ALP. 07:58: Not felt/no damage potential Not felt/no damage potential good BRE. 07:59: Not felt/no damage potential Not felt/no damage potential very good DJJ. 07:58: Not felt/no damage potential Not felt/no damage potential very good DLA. 07:59: Not felt/no damage potential Not felt/no damage potential good LCG. 07:58: Not felt/no damage potential Not felt/no damage potential very good LFP. 07:58: weak/no damage potential weak/no damage potential good MOP. 07:58: weak/no damage potential Not felt/no damage good RIN. 07:58: Not felt/no damage potential Not felt/no damage potential very good RIO. 07:59: Not felt/no damage potential Not felt/no damage potential very good SMS. 07:58: Not felt/no damage potential Not felt/no damage potential very good STC. 07:58: Not felt/no damage potential Not felt/no damage potential very good STS. 07:59: Not felt/no damage potential Not felt/no damage potential good VCS. 07:58: Not felt/no damage potential Not felt/no damage potential very good WSS. 07:58: weak/no damage potential weak/no damage potential good Summary (of GM prediction): very good 57.1 % good 42.9 % Percentage of reporting stations at given performance level I(t) = Σ I stat N stat I f = Σ I(t) duration time event

19 Future works In next few moths we will apply the algorithms to different networks to evaluate how algorithm performances are related with network features We will compare all algorithms using the quality factor criteria as shown

20 Thank you for your attention

23 Distinguishing between P- and S-waves PS =0.43 log(z a )+0.55 log(z v ) 0.46 log(en a ) 0.55 log(en v ) if P S > 0 P-wave; if P S < 0 s-wave;

24 Average Rock and Soil envelopes as functions of M, R RMS horizontal acceleration

Estimating M from ratios of ground motion P-wave frequency content scales with M (Allen and Kanamori, 2003, Nakamura, 1988) Find the linear combination of log(acc) and log(disp) that minimizes the

25 Estimating M from ratios of ground motion P-wave frequency content scales with M (Allen and Kanamori, 2003, Nakamura, 1988) Find the linear combination of log(acc) and log(disp) that minimizes the variance within magnitude-based groups while maximizing separation between groups (eigenvalue problem) Z ad = 0.36log(acc) 0.93log(disp) 0.36 acc = log disp 0.93 Estimating M from Zad M P = 1.627Z ad +8.94, σ MP =0.45 M S = 1.459Z ad +8.05, σ MS =0.41

26 Are we mixing apples and oranges? PGA PGV M7.5 M7.5 M3.5 M3.5 10/4/07

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