International conference SATREPS project JAPAN PERU Cooperation
for Earthquake and Tsunami Disaster Risk Reduction

Seismic Source Model and StrongMotion Simulation in Lima
Nelson Pulido
National Research Institute for Earth Science and Disaster
Prevention“Enhancement of earthquake and tsunami technologies inPeru”
1Contenido• Sísmos históricos en los Andes
Centrales Perú• Elaboración de escenarios para
un posible mega terremoto parala ciudad de Lima.
• Estimación de la amenaza
sísmica para Lima a raíz de laocurrencia de un megaterremoto.
2Historical earthquakes in Peru
17461868
Nazca ridge265 years
143 years
Adapted from Okal et al (2006)

1940 05 24 Huacho, M8.2
1942 08 24 San Juan, M8.0
1966 10 17 Barranca, M8.1
1974 10 03 Lima, M8.1
2001 06 23 Atico, M8.4
2007 08 15 Pisco, M8.0

Sladen et al. (2010)
3

Slip deficit rate for Peru and Northern Chileand scenario earthquake for Central Peru
(Chlieh et al. 2011)• Slip deficit since 1746 (265 years)• Maximum slip is 20 m
• Magnitude Mw~8.9, neglecting the
20 century earthquake sequence(Pulido et al. 2011)

C(x)*vo

S(x)=C(x) * vo * t
S(x): slip, t : elapsed time4

BB slips realizations using Von Karman PSD

5

Hypocenter locations scenarios

LimaPisco6

Strong Motion simulation methodology
and estimation of source parameters
Source parameters for mostcritical slip scenario−πfRij Q( f ) βAij ( f ) =Rpij (θ , φ, f )M0S ( f , Δσ i )G( f )eP( f , fmax)

4πρβ3Rij• Strong motion simulation is basedon a broadband frequency finite
fault methodology [Pulido et al.
2004, 2007, 2012]• Engineering bedrock groundmotions are obtained by convolving
seismic bedrock (rock) simulation
with 1D soil transfer functions.
7Array microtremors measurements in Lima, Peru, andinstallation of a strong motion network [SATREPS project]

Small Arrays
Large Arrays
New Seismometers

Observation sites in Lima

Dispersion curve and estimated Vs profile

Average PGA for each slip scenario

30 km

Slip 5• PGA correspond to the average for all starting points of rupture for eachscenario slip, at the engineering bedrock (Vs~400 m/s).
9Average S‐wave velocity for the upper
10m and soil amplifications toengineering bedrock (Vs ~ 400m)

10Average PGA and PGV for worst caseslip scenario

11Most critical scenario & Observed intensities
during the 1746 earthquake (M～9)~IX

PGA for most critical slip, includes
soil amplification

Maximum MMI intensity in Lima IX~X
(Alva et al. 1984)
PGA ~1g, PGV ~90 cm/s
12

Simulated engineering bedrock PGA andProbabilistic Seismic Hazard Maps (PSHM) of Peru
Average PGA for all
slip scenarios

Average PGA for mostcritical slip scenario• PSHM of Peru corresponds to a 10% probability of exceedence in 50 years(Castillo & Alva, 1993)
13

Average Pseudo
acceleration response
spectra (Psa) for all
scenarios and selectedperiods (h=0.05)

Frequency dependent site
amplifications (Sekiguchi et al. 2013)14

Average velocity
response spectra(Sv) for all
scenarios andselected periods(h=0.05)

15

Simulated Pseudo acceleration response spectra(average for all scenarios) at all grid points and the
Peruvian seismic building code design spectra (2003)

Peruvian building code for the highest seismic zone (Z), soft soils (Tp, S),essential building type (U), and the lowest seismic reduction factor (R)16Conclusions• We developed a methodology for the estimation of slip
scenarios for a future megathrust earthquake based on aninterseismic coupling model at the megathrust as well as
information of historical earthquakes.
• Central Andes, Peru has currently a seismic potential foran earthquake with magnitude Mw~8.9, corresponding toa source area of about 500 km by 165 km.
• We simulated strong ground motions in Lima obtaining
PGA and PGV values as large as 1000 cm/s2 and 100 cm/s,
which is in agreement with observed macro-seismic
intensities in Lima during the 1746 earthquake (MMIIX~X).
1718

Historical megathrust earthquakes inSouth America
5.5cm/y
6.5 cm/y
6.5
cm/y

7.0
cm/y

EcuadorColombia

N
19

Seismic coupling and mega‐earthquakes
Coupling model

Slip model

Slip model

Coupling model

Source model of the 2011 (M9.0)
Source model of the 2010 (M8.8)
Maule earthquake, Chile (Pulido et Tohoku-oki earthquake, Japan (Suzukiet al. 2011), and seismic couplingal. 2011), and seismic coupling(Loveless and Meade, 2012)(Moreno et al. 2010)20

Spectral characteristics of slip deficit
Power Spectral Density [PSD] (Pulido et al. 2011)

Von Karman Power spectral density function• Correlation length along strike: as = 110 km• Correlation length along dip:ad = 40 km• Hurst exponent:
H =1.0• ks and kd are wavenumbers along strike anddip
21

Construction of a broadband
wavelength slip
長波長

短波長

Amplitude and phasefrom geodetic slip

VK PSD withrandom phase

22

Soil amplification in metropolitan areas ofTokyo and Lima cities
Tokyo (from NIED/J-SHIS HP)
Lima

23

Standard deviations of PGA and PGV forground motions from all scenarios

24Simulated Velocity response spectra (average for allscenarios) at all grid points (~250m)
CallaoVilla El Salvador

MOL
DHN
PPIMOLDHN

Callao

PPI, MOL, DHN; T= 0.3s, MOL, DHN; T= 0.3s
Callao and Villa el Salvador; T=1.0 - 1.5s
25Comparison of the ground motion simulated for one of ourmega‐earthquake scenarios with the observed records
obtained during the 1974 y 1966 earthquakes in Lima

Slip scenario No.5
and 7 and starting
point of the rupture at
the southern edge ofthe fault

PQR

PQR

26

Comparison between spectral characteristics of the 2010
Maule earthquake M8.8 and slip scenario for Central
Andes[Mw 8.9][Mw 8.9][Pulido et al. 2011]27

Simulated PGVat Lima for all
rupture starts• Slip model No. 528

Velocity models
obtained frommicrotremors
arrays in Lima andtheir 1D transfer
functions (Calderónet al. 2012)29