deterministic strong ground motions
TRANSCRIPT
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Deterministic Seismic Ground Motions and the PEER NGA Ground Motion Prediction Equations (GMPEs)
David M. Boore
EERI Utah Chapter Short Course on Seismic Ground MotionsWest Jordan, UtahMarch 05, 2015
1David M. Boore
http://www.daveboore.com/presentations.html
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Contents of the Lecture• Overview of Ground‐Motion Prediction Equations (GMPEs)
• Predicted and Predictor Variables• The PEER NGA‐West2 GMPEs
• Data• Functions• Comparisons of median predictions• Aleatory and Epistemic Uncertainties
• Comparing Greek Data to NGA‐West2 Predictions• Present and Future Work• Resources
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Ground‐Motion Prediction Equations (GMPEs): What are they?
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Ground‐Motion Prediction Equations (GMPEs): How are they used?
• Engineering: Specify motions for seismic design (critical individual structures as well as building codes)
• Seismology: Convenient summary of average M and R variation of motion from many recordings
• Source scaling• Path effects• Site effects
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Developing GMPEs requires knowledge of:
• Data acquisition and processing• Source physics• Velocity determination• Linear and nonlinear wave propagation• Simulations of ground motion• Model building and regression analysis
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How are GMPEs derived?• Collect data
• Choose functions (keeping in mind the application of predicting motions in future earthquakes).
• Do regression fit
• Study residuals
• Revise functions if necessary
• Model building, not just curve fitting
P. Stafford 6David M. Boore
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Considerations for the functions
• “…as simple as possible, but not simpler..” (A. Einstein)
• Give reasonable predictions in data‐poor but engineering‐important situations (e.g., close to M≈8 earthquakes)
• Use simulations to guide some functions and set some coefficients (an example of model building, not just curve fitting)
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Predicted and Predictor Variables• Ground‐motion intensity measures
– Peak acceleration– Peak velocity– Response spectra
• Basic predictor variables– Magnitude– Distance– Site characterization
• Additional predictor variables– Basin depth– Hanging wall/foot wall– Depth to top of rupture– etc.
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Input Parameters in PEER Spreadsheet: Source
• Moment magnitude• Fault Width• Fault Dip• Fault Type
– Unspecified– Strikeslip– Normal– Reverse
• Depth to Top of Rupture • Hypocentral Depth• Aftershock or Mainshock?
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Input Parameters in PEER Spreadsheet: Site
• VS30
• Was VS30 measured or inferred? (Needed for uncertainty calculation)• Depth to 1.0 km/s shear‐wave velocity• Depth to 2.5 km/s shear‐wave velocity
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Input Parameters in PEER Spreadsheet: Path
• Region• RJB• RRUP• RX• RY0
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Wave Type and Frequencies of Most Interest
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• Seismic shaking in range of resonant frequencies of structures
• Shaking often strongest on horizontal component:– Earthquakes radiate larger S waves than P waves– Refraction of incoming waves toward the vertical S waves primarily horizontal motion
• Buildings generally are weakest for horizontal shaking• GMPEs for horizontal components have received the most attention
Horizontal S waves are most important for engineering seismology:
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Frequencies of ground‐motion for engineering purposes
• 20 Hz ‐‐‐ 10 sec (usually less than about 3 sec)
• Resonant period of typical N story structure ≈ N/10 sec– What is the resonant period of the building in which we are located?
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What are response spectra?
• The maximum response of a suite of single degree of freedom (SDOF) damped oscillators with a range of resonant periods for a given input motion
• Why useful? Buildings can often be represented as SDOF oscillators, so a response spectrum provides the motion of an arbitrary structure to a given input motion
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Period = 0.2 s
0.5 s 1.0 s
Courtesy of J. Bommer16David M. Boore
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Response Spectrum
Courtesy of J. Bommer17David M. Boore
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PGA generally a poor measure of ground‐motion intensity. All of these time series have the same PGA:
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But the response spectra (and consequences for structures) are quite different:
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Design maps in building codes are for T=0.2 and T=1.0 s
• Design values at other periods are obtained by anchoring curves to the T=0.2 and T=1.0 s values, as shown in the next slide
• But PGA useful in liquefaction analysis (along with duration)
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0.0 0.5 1.0 1.5 2.0Period
0.0
0.5
1.0
1.5
2.0
2.5
Spec
tralR
espo
nse
Acce
lera
tion 0.0 0.5 1.0 1.5 2.0
0.0
0.5
1.0
1.5
2.0
2.5
Construct response spectrum at all periods using T = 0.2 and 1.0 sec values
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Computing RotD50• Project the two as‐recorded horizontal time series into azimuth Az
• For each period, compute PSA, store Az, PSA pairs in an array
• Increment Az by δα and repeat first two steps until Az=180
• Sort array over PSA values• RotD50 is the median value• RotD00, RotD100 are the minimum and maximum values
• NO geometric means are used
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To convert GMPEs using random component as the IM (essentially, the as‐recorded geometric mean), multiply by RotD50/GM_AR
To convert GMPEs using GMRotI50 as the IM (e.g., 2008 NGA GMPEs), multiply by RotD50/GMRotI50
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Strike-normal (also known as “fault-normal”) motion only close to maximum
motion within about 3 km of the fault
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What to use for the basic predictor variables?
• Moment magnitude– Best single measure of overall size of an earthquake (it does not saturate)
– It can be estimated from geological observations– Can be estimated from paleoseismological studies– Can be related to slip rates on faults
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What to use for the basic predictor variables?
• Distance – many measures can be defined
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What to use for the basic predictor variables?
• Distance– The distance measure should help account for the extended fault rupture surface
– The distance measure must be something that can be estimated for a future earthquake
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What to use for the basic predictor variables?
• Distance – not all measures useful for future events
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Most Commonly Used:RRUP
RJB (0.0 for station over the fault)
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Courtesy: Jennifer Donahue
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What to use for the basic predictor variables?
• A measure of local site geology
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Site Classifications for Use WithGround-Motion Prediction Equations
• Rock = less than 5m soil over “granite”, “limestone”, etc.• Soil= everything else
2. NEHRP Site Classes (based on VS30)
3. Continuous Variable (VS30)
1. Rock/Soil
620 m/s = typical rock
310 m/s = typical soil
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0 ( )
SZ z
S
zVd
V
0
1 1 ( )z
SZ SSZ
S S dV z
Time-averaged shear-wave velocity to depth z:
Average shear-wave slowness to depth z:
37David M. Boore
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Why VS30?
• Most data available when the idea of using average Vs was developed were from 30 m holes, the average depth that could be drilled in one day
• Better would be Vsz, where z corresponds to a quarter‐wavelength for the period of interest, but
• Few observations of Vs are available for greater depths
• Vsz correlates quite well with Vs30 for a wide range of z greater than 30 m (see next slide, from Boore et al., BSSA, 2011, pp. 3046—3059)
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Vsz correlates quite well with Vs30 for a wide range of z greater than 30 m
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But regional differences exist
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VS Profiles for VS30=270 (± 5%)
CALIFORNIA JAPAN
From Kamai et al (2015)Abrahamson (2015)
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0 500 1000 15000.1
0.2
1
2
10
index (sorted by V30)
obse
rved
/pre
dict
ed(n
osi
teco
rrect
ion)
class Dclass Cclass B
T = 2.0 sec, rjb < 80 km
File
:C:\p
eer_
nga\
team
x\re
sids
_dm
b_m
r6_6
_by_
clas
s_t2
p0.d
raw
;D
ate:
2005
-04-
20;T
ime:
16:3
7:55
Large scatter would remain after removing site effect based on site class
Knowing site class shifts aleatory to epistemic uncertainty
This figure is also a good representation of the data availability for various site classes: most data are from class D, very few from class B (rock) 42David M. Boore
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200 1000 20000.40.5
1
2
345
V30, m/sec
Am
plifi
catio
n
T=0.10 s
slope = bv, where Y (V30)bV
200 1000 20000.40.5
1
2
345
V30, m/sec
T=2.00 s
VS30 as continuous variable
Note period dependence of site response43David M. Boore
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Effect of different site characterizationsfor a small subset of data for which V30values are available
• No site characterization• Rock/soil• NEHRP class• V30 (continuous variable)
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-0.5
0
0.5
1
1.5
Obs
-BJF
(M,R
) RockSoil
T = 2.0 secResiduals computed as obs-bjf (with no site correction,which is the same as assuming BJF class A or V30 = VA).
200 300 400 1000-1
-0.5
0
0.5
1
V30 (m/sec)
Obs
-BJF
(M,R
)-S
ite(r
ock,
soil)
RockSoil
T = 2.0 secrock & soil site classification
File
:C:\p
sv\D
EVEL
OP\
RSD
L2A_
B_pp
t.dra
w;
Dat
e:20
03-0
6-13
;Ti
me:
19:0
7:42
σ=0.25
σ=0.25
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-1
-0.5
0
0.5
1
Obs
-BJF
(M,R
)-S
ite(B
,C,D
)
RockSoil
T = 2.0 secNEHRP B,C,D site classification
200 300 400 1000-1
-0.5
0
0.5
1
V30 (m/sec)
Obs
-BJF
(M,R
)-S
ite(V
30) Rock
Soil
T = 2.0 secV30 site classification (continuous)
File
:C:\p
sv\D
EVEL
OP\
RSD
L2C
_D_p
pt.d
raw
;D
ate:
2003
-06-
13;T
ime:
19:0
8:04
σ=0.21
σ=0.20
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2002 M 7.9 Denali Fault
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K2-16
1741
K2-03
K2-06K2-09
K2-11K2-12
K2-14
K2-22
1744
1751
K2-02
K2-13
1734
1737
K2-01
K2-04
K2-05
K2-07
K2-19
1397
1731
1736
K2-08
K2-20K2-21
-150 -149.8
61.1
61.2
Longitude (oE)
Latit
ude
(o N)
D
C/D C
Chu
gach
Mts
.
Site Classes are based on the average shear-wave velocity in the upper 30 m
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Remove high frequencies by filtering to emphasize similarity of longer‐period waveforms
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0.1 1 100.001
0.01
0.1
1
10
100
Frequency (Hz)
Four
ier
Acc
eler
atio
n(c
m/s
ec)
Denali: EWclass Dclass C/Dclass Cclass B (one site)
range of previous site response studies
0.1 1 100.1
0.2
1
2
Frequency (Hz)R
atio
(rel
ativ
eto
avg
C)
Denali: EWclass Dclass C/Dclass Cclass B (one site)
range of previous site response studies
File
:C:\a
ncho
rage
_gm
\fas_
and_
ratio
_EW
_avg
_ref
_cc_
4ppt
.dra
w;
Dat
e:20
05-0
4-19
;Ti
me:
17:1
9:5
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•Strong correlation between Ztor and M
•Most M>7 earthquakes reach the surface (but no data for normal-fault earthquakes with M>7)
Correlation between Ztor, mechanism, and M
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Stewart et al., PEER 2013/22; Courtesy of Y. Bozorgnia
GMPEs: The Past
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The large epistemic variations in predicted motions are not decreasing with time
From Douglas (2010)
Mw6 strike-slip earthquake at rjb = 20 km on a NEHRP C site
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(M=6, R=20 km)
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GMPEs: The Present
• Illustrate Empirical GMPEs with PEER NGA‐West 2
• (NGA = Next Generation Attenuation relations, although the older term “attenuation relations” has been replaced by “ground‐motion prediction equations”)
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PEER NGA‐West 2 Project Overview• Developer Teams (each developed their own GMPEs)
• Supporting Working Groups– Directivity– Site Response– Database– Directionality– Uncertainty– Vertical Component– Adjustment for Damping
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NGA-West2 Developer Teams:
• Abrahamson, Silva, & Kamai (ASK14)• Boore, Stewart, Seyhan, & Atkinson (BSSA14)• Campbell & Bozorgnia (CB14)• Chiou & Youngs (CY14)• Idriss (I14)
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PEER NGA‐West 2 Project Overview
• All developers used subsets of data chosen from a common database – Metadata– Uniformly processed strong‐motion recordings– U.S. and foreign earthquakes– Active tectonic regions (subduction, stable continental regions are separate projects)
• The database development was a major time‐consuming effort
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Observed data generally adequate for regression, but note relative lack of data for distances less than 10 km. Data are available for few large magnitude events.
NGA-West2 database includes over 21,000 three-component recordings
from more than 600 earthquakes
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Observed data not adequate for regression, use simulated data (the subject of a different lecture)
Observed data adequate for regression exceptclose to large ‘quakes
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Restricted to data within 80 km with at least 4 recordings per event
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NGA‐West2 PSAs for ss events (adjusted to Vs30=760 m/s) vs. RRUP
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nrecs = 11,318 for TOSC=0.2 s; nrecs = 3,359 for TOSC=6.0 s
David M. Boore
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NGA‐West2 PSAs for ss events (adjusted to Vs30=760 m/s) vs. RRUP
65
There is significant scatter in the data, with scatter being larger for small earthquakes.
David M. Boore
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NGA‐West2 PSAs for ss events (adjusted to Vs30=760 m/s) vs. RRUP
66
For a single magnitude and for all periods the motions tend to saturate for large earthquakes as the distance from the fault rupture to the observation point decreases.
David M. Boore
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NGA‐West2 PSAs for ss events (adjusted to Vs30=760 m/s) vs. RRUP
67
At any fixed distance the ground motion increases with magnitude in a nonlinear fashion, with a tendency to saturate for large magnitudes, particularly for shorter period motions. To show this, plot PSA within the bands vs. M.
David M. Boore
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NGA‐West2 PSAs for ss events (adjusted to Vs30=760 m/s) vs. RRUP
68
At any fixed distance (centered on 50 km here, including PSA in the 40 km to 62.5 km range) the ground motion increases with magnitude in a nonlinear fashion, with a tendency to saturate for large magnitudes, particularly for shorter period motions. PSA for larger magnitudes is more sensitive to M for long‐period motions than for short‐period motions David M. Boore
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David M. Boore 69
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David M. Boore 70
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NGA‐West2 PSAs for ss events (adjusted to Vs30=760 m/s) vs. RRUP
71
For a given period and magnitude the median ground motions decay with distance; this decay shows curvature at greater distances, more pronounced for short than long periods.
(lines are drawn by eye and are intended to give a qualitative indication of the trends)David M. Boore
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Characteristics of Data that GMPEs need to capture
• Magnitude‐distance distribution depends on region• Change of amplitude with distance for fixed magnitude• Change of amplitude with magnitude after removing distance dependence
• Site dependence (including basin depth dependence and nonlinear response)
• Earthquake type, hanging wall, depth to top of rupture, etc.
• Scatter
72David M. Boore
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In 1994• Typical functional form of GMPEs
(Courtesy of Yousef Bozorgnia)
(Boore, Joyner, and Fumal, 1994)
73David M. Boore
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In 2014
(Courtesy of Yousef Bozorgnia)74David M. Boore
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Boore, Stewart, Seyhan, and Atkinson (BSSA14) GMPEs• General form:
• M scaling
• Distance scaling:
20 1 2 3 4 5, E h hF mech e U e SS e NS e RS e eM M M M M
0 1 2 3 6,E hF mech e U e SS e NS e RS e M M M
M ≤ Mh:
M >Mh:
30 1
30
ln , , , , , , ,
, ,E P JB S S JB
n JB S
Y F mech F R region F V R region z
R V
M M M
M
75Modified from a slide by E. Seyhan
1 2 3 3, , ln /P JB ref ref refF R region c c R R c c R R M M M
2 2JBR R h
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• General form:
• M scaling
• Distance scaling:
20 1 2 3 4 5, E h hF mech e U e SS e NS e RS e eM M M M M
0 1 2 3 6,E hF mech e U e SS e NS e RS e M M M
M ≤ Mh:
M >Mh:
30 1
30
ln , , , , , , ,
, ,E P JB S S JB
n JB S
Y F mech F R region F V R region z
R V
M M M
M
76Modified from a slide by E. Seyhan
1 2 3 3, , ln /P JB ref ref refF R region c c R R c c R R M M M
2 2JBR R h
focal mechanism quadratic M-scaling
linear M-scaling
Boore, Stewart, Seyhan, and Atkinson (BSSA14) GMPEs
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BSSA14 GMPEs
• General form:
• M scaling
• Distance scaling:
20 1 2 3 4 5, E h hF mech e U e SS e NS e RS e eM M M M M
0 1 2 3 6,E hF mech e U e SS e NS e RS e M M M
M ≤ Mh:
M >Mh:
30 1
30
ln , , , , , , ,
, ,E P JB S S JB
n JB S
Y F mech F R region F V R region z
R V
M M M
M
77Modified from a slide by E. Seyhan
1 2 3 3, , ln /P JB ref ref refF R region c c R R c c R R M M M
2 2JBR R h
M-indep.
M-dependent distance decay
pseudo depth
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BSSA14 GMPEs
• General form:
• M scaling
• Distance scaling:
20 1 2 3 4 5, E h hF mech e U e SS e NS e RS e eM M M M M
0 1 2 3 6,E hF mech e U e SS e NS e RS e M M M
M ≤ Mh:
M >Mh:
30 1
30
ln , , , , , , ,
, ,E P JB S S JB
n JB S
Y F mech F R region F V R region z
R V
M M M
M
78Modified from a slide by E. Seyhan
1 2 3 3, , ln /P JB ref ref refF R region c c R R c c R R M M M
2 2JBR R h anelastic attenuation
regional corrections
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BSSA14 GMPEs
• Site term
Linear site term
Nonlinear site term
31 2
3
2 30 4 5 30 5
ln( ) *ln
, ( ) exp ( )* min ,760 360 exp ( )* 760 360
nl
s s
PGAr fF f ff
f V T f T f T V f T
3030
30
ln
ln
ln
SS c
ref
lin
cS c
ref
Vc V VV
FVc V VV
130 1 1 , , , , ln ln ( , )S S JB lin nl zF V R z region F F F z region M
VS30-scaling
Break velocity
Slope of nonlinearity
Vs30 (m/s)
ln(F
lin)
PGAc
1
Vc
PGAr (g)ln
(Fnl
)
soft soil
stiff soil
PGA
f2 1
79Modified from a slide by E. Seyhan
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80David M. Boore
Why ln Y? Residuals are log-normally distributed
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Adding BSSA14 curves to data plots shown before
81David M. Boore
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• Need complicated equations to capture effects of:– M: 3 to 8.5 (strike‐slip)– Distance: 0 to 300km– Hanging wall and footwall sites– Soil VS30: 150‐1500 m/sec– Soil nonlinearity– Deep basins– Strike‐slip, Reverse, Normal faulting mechanisms– Period: 0‐10 seconds
• The BSSA14 GMPEs are probably the simplest, but there may be situations where they should be used with caution.
Courtesy of Yousef Bozorgnia)82David M. Boore
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83David M. Boore
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Model Applicability
84David M. Boore
Developer M R Vs30
ASK14 3.0‐8.5 0‐300 180‐1000
BSSA14 3.0‐8.5 0‐400 150‐1500
3.0‐7.0 (NS) 0‐400 150‐1500
CB14 3.3‐8.5 (SS) 0‐300 150‐1500
3.3‐8.0 (RS) 0‐300 150‐1500
3.3‐7.5 (NS) 0‐300 150‐1500
CY14 3.5‐8.5 (SS) 0‐300 180‐1500
3.5‐8.0 (RS, NS) 0‐300 180‐1500
I14 >=5.0 0‐150 >=450
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Comparisons of NGA‐West1 and NGA‐West2 results for each developer
David M. Boore 85
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David M. Boore 88
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David M. Boore 89
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David M. Boore 90
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Comparisons of NGA‐West2 results for the five developer teams
David M. Boore 91
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92David M. Boore
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93David M. Boore
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94David M. Boore
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95David M. Boore
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96David M. Boore
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97David M. Boore
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98David M. Boore
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Surface outcrop, 45 degree dip to right, to 15 km depth99David M. Boore
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Quantifying the Uncertainty
• The GMPEs predict the distribution of motions for a given set of predictor variables
• The dispersion about the median motions can be crucial for low annual-frequency-of-exceedance hazard estimates (rare occurrences for highly critical sites, such as nuclear power plants, nuclear waste repositories)
• Must be clear on type of uncertainty• The scatter is very large; can it be reduced?
100David M. Boore
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101David M. Boore
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BSSA14 GMPEs
• Aleatory uncertainty
2 230 30 , , , ,JB S JB SR V R V M M M
1
1 2 1
2
4.5 4.5 4.5 5.5
5.5
MM M M
M
30, ,JB SR V M
between-event aleatoryuncertainty
within-event aleatory uncertainty
Total aleatory uncertainty
102Modified from a slide by E. Seyhan
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Stage 1 Regression: Determine the distance function and event offsets
103David M. Boore
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Stage 2 Regression: Determine the magnitude scaling
104David M. Boore
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105David M. Boore
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106
Example of comparison of total standard deviations
Gregor et al., 2014)
David M. Boore
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107David M. Boore
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108
Al Atik and Youngs, 2014)
David M. Boore
Epistemic uncertainty for normal faults
Model-to-model variability is generally larger than the uncertainty in the medians of each model
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Comparisons of Greek Data and the NGA‐W2 GMPEs
• Residual = ln Observed – ln Predicted from NGA‐W2
• For each period, use mixed effects regression to separate residuals into – 1) overall bias– 2) earthquake‐to‐earthquake (inter‐earthquake) variation
– 3) within earthquake (intra‐earthquake) variation
109David M. Boore
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Mixed effects regressions
i<0
ij
iji>0
EQID: 1
EQID: 2
ij
30ln , , ,
| EQID
The random part fits a mean to each of the random groups defined in EQID( ) 0 var( )
ij ij ij JB S
ij k i ij
ij ij ij
R Y M mech R V
R c
vector IID randomerror terms with mean E and Z
110
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Greek Data compared with NGA‐W2 GMPEs
Residual=ln Observed – ln NGA‐W2
Except for Vs30 dependence for small Vs30, overall dependence of Greek and NGA‐W2 predictions are similar
111David M. Boore
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Significant overall bias for sort periods (max factor=0.68)—due to filtering effects of structures from which records were obtained?
112David M. Boore
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GMPEs: The Future
• Future PEER NGA Work • Using simulations to fill in gaps in existing recorded motions
113David M. Boore
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NGA-West2/3
Vertical component (finished)
Add directivity
NGA-East
For stable continental regions
2015
NGA-Sub
For subduction regions
2016
NGA: 2014 and beyond
Slide from Y. Bozorgnia114David M. Boore
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Vertical/Horizontal (from SBSA14/BSSA14)
115
Magnitude Dependence VS30 Dependence
NOTE: Rule of thumbV/H=2/3 OK only for RJB=70 km, VS30= 760 m/s
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New data may not fill in gaps in data in the near term, particularly close to large earthquakes and for important fault‐site geometries, such as over the hanging wall of a reverse‐slip fault.
116David M. Boore
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New data may not fill in gaps in data in the near term, particularly close to large earthquakes and for important fault‐site geometries, such as over the hanging wall of a reverse‐slip fault.
117David M. Boore
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New data may not fill in gaps in data in the near term, particularly close to large earthquakes and for important fault‐site geometries, such as over the hanging wall of a reverse‐slip fault.
118David M. Boore
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Use of Simulated Motions
• Supplement observed data and derive GMPEs from the combined observed and simulated motions
• Constrain/adjust GMPEs for things such as:– Hanging wall– Saturation– Directivity– Splay faults and complex fault geometry– Nonlinear soil response
119David M. Boore
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Resources
• Web Sites– peer.berkeley.edu/ngawest2/
• peer.berkeley.edu/ngawest2/final-products/• peer.berkeley.edu/ngawest2/databases/
– www.daveboore.com• Papers• Software for evaluating GMPEs
120David M. Boore
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Resources
• Paper and Reports – 2013 PEER Reports (from PEER web site)– 2014 Earthquake Spectra Papers (H
component papers published in vol. 30(3), August, 2014)
121David M. Boore
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Resources
• Programs to evaluate GMPEs– NGA-West2 GMPEs Excel file (from
databases page of NGA-West2 web site)– http://www.daveboore.com/pubs_online.ht
ml (Fortran programs available under the entry for BSSA14)
– Matlab code for ASK14 from EqSsupplement
122David M. Boore
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Thank You
123David M. Boore