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Evaluation of Ground Motion Prediction Equations (GMPEs) for Chile Subduction Zone

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In this study we present an evaluation of the performance of the latest ground motion prediction equations for Chilean subduction zone. Ground-motion prediction equations (GMPEs) for earthquakes that occur in subduction zones are a key input to seismic hazard analyses and risk mitigation. In this study, we use recent large subduction events recorded in Chile, including Valparaiso (Mw 7.9), Maule (Mw 8.8), Iquique (Mw 8.1) and Southern Peru (Mw 8.4). We use shear-wave velocity profiles that were obtained for several stations through surface wave dispersion methods using active and passive sources, this represents a significant contribution to earthquake hazard mitigation in Chile, as this information is useful for a number of projects that tribute towards that end. All records were processed following state of the art procedures to obtain comparable signal to noise level and detrended records, the processing was performed component by component. Response spectra of the recorded ground motions were compared with those predicted by BCHydro [1], Ruiz & Saragoni [2], Atkinson & Boore [3][3], Boroscheck & Contreras [5] and, Zhao et al. [6] ground motion prediction models. Results show that at low frequencies BCHydro has the best predictive capacity, while Zhao et al. is the best performing GMPE for high frequencies. Most of the tested GMPEs underestimate spectral accelerations.
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Evaluation of Ground Motion Prediction
Equations (GMPEs) for Chile Subduction
Zone.
Nicolás BASTÍAS a, Gonzalo A. MONTALVA a,1, Felipe LEYTON b, Esteban SAEZ c,
Francisco RUZd and Pedro TRONCOSOa.
a Universidad de Concepción, Concepción, Chile
b Centro Sismológico Nacional, Santiago, Chile
c Pontificia Universidad Católica de Chile, Santiago, Chile
d Ruz y Vukasovic Ingenieros, Santiago, Chile
Abstract. : In this study we present an evaluation of the performance of the latest
ground motion prediction equations for Chilean subduction zone. Ground-motion
prediction equations (GMPEs) for earthquakes that occur in subduction zones are a
key input to seismic hazard analyses and risk mitigation. In this study, we use
recent large subduction events recorded in Chile, including Valparaiso (Mw 7.9),
Maule (Mw 8.8), Iquique (Mw 8.1) and Southern Peru (Mw 8.4). We use shear-
wave velocity profiles that were obtained for several stations through surface wave
dispersion methods using active and passive sources, this represents a significant
contribution to earthquake hazard mitigation in Chile, as this information is useful
for a number of projects that tribute towards that end. All records were processed
following state of the art procedures to obtain comparable signal to noise level and
detrended records, the processing was performed component by component.
Response spectra of the recorded ground motions were compared with those
predicted by BCHydro [1], Ruiz & Saragoni [2], Atkinson & Boore [3][3],
Boroscheck & Contreras [5] and, Zhao et al. [6] ground motion prediction models.
Results show that at low frequencies BCHydro has the best predictive capacity,
while Zhao et al. is the best performing GMPE for high frequencies. Most of the
tested GMPEs underestimate spectral accelerations.
Keywords. Ground Motions, Megathrust Earthquake Intensities, Chile,
Subduction Zone GMPEs, Ground Motion Prediction Equations.
Introduction
Ground motion prediction equations (GMPEs) are a key component and affect
significantly probabilistic seismic hazard analyses (PSHA) outcomes. Therefore,
evaluate the performance of suitable GMPE models in the region will allow appropriate
selection and weighting of them, better seismic hazard analyses and ultimately lower
earthquake related risk. The GMPEs herein analyzed were obtained in subduction
tectonic environments, the GMPEs selected were Atkinson & Boore [3][4], BCHydro
[1], both of which use world data to fit their models, Zhao et al. [6] that was drawn
from Japanese data, and finally, used two models with only Chilean data; Ruiz &
Saragoni [2] and, Boroschek & Contreras [5].
A dataset including Mw from 5 to 8.8 was compiled, including the largest Chilean
strong motion records. This allows developing a robust evaluation of GMPE in the
1Address: Edmundo Larenas 219, Concepción 4030000, Chile | Email: gmontalva@udec.cl |
Phone: (+56) 41 220 4446
Chilean subduction prone region. The records were processed per component and type
of record (i.e. analog or digital) as described in the next section.
The performance of each GMPE is evaluated using the likelihood approach and
average sample log-likelihood (LLH) values by Scherbaum [7] [8]. The first is based in
the statistics of the distributions of the normalized residuals between predicted values
from GMPEs and observations. The second approach [8], calculates the average sample
log-likelihood differences (as estimators for the Kullback-Leibler (KL) differences, that
represented the difference between two probability distributions and , this is a
measure of the loss of information when is used to estimate , in our case
represents the GMPE model and the observed ground motion data. Residuals were
computed for PGA and pseudo-spectral accelerations at 0.1, 0.4, 1.0 and, 2 seconds.
Inslab and interplate events were analyzed dividing the data as two different subsets.
1. Available Data
A key issue to evaluate ground motion prediction equations is the compilation of a
dataset of empirical ground motion records. For this work, ground motions from Chile
have been selected and processed.
1.1. Ground Motion and Event Characterization
The event information was collected from scientific literature and national/international
seismic agencies. For epicentral locations and depths we used the Centro Sismológico
Nacional (CSN) reports, all events within the dataset are reported by this agency. The
moment magnitudes (Mw) were obtained from the Harvard Centroid Moment Tensor
(CMT, [9]), for events that are not available by CMT we used other magnitude scales
(e.g. Ms) reported by CSN or by International Seismological Centre (ISC), and use
conversion equations by Leyton et al. [10] to convert to Mw.
To define the seismogenesis (i.e., interface, inslab or crustal earthquakes) we first
used the moment tensor reported by the CMT catalog; interface events are associated to
reverse faulting and, inslab events are mostly normal. The above information is
supported also with hypocentral location, it is expected that interface events are located
between the subduction trench and the Chile’s continental coast at ~40-50 km depth, in
the case of inslab are associated to intermediate depths and epicentral location inside
continental Chile.
Closest distance to the fault rupture plane (Rrup) of each event was computed from
finite fault rupture models, compiled by SRCMOD website [11], whenever available.
For events with no published finite fault solution we estimated a rupture plane with the
relationships provided by Strasser et al. [12] that predict the length and width based in
moment magnitude for subduction zone, and located the center of rupture plane in the
centroid reported by CMT. The rupture plane was oriented using the strike and dip
from the CMT catalog.
1.2. Characterization Ground Motion Sites
The main site parameter used by modern GMPEs is Vs30, hence these values and other
geophysical parameters, like predominant frequency (f0), and shear-wave velocity
profile (Vsx) were sought. Efforts to obtain these parameters and characterize the
Chilean GM stations were led by the authors and the Chilean Foundation for
Geotechnical Research (FUCHIGE, [13]); due to space constraints we do not show the
obtained values but they can be accessed freely at [13]. We measured ambient noise
with triaxial geophones, performed active and passive array surveys to invert shear-
wave velocity profiles in more than 20 stations, and referred to the literature ([14]-[17])
for other stations.
For sites without measured Vs30 value, we used their predominant frequency (f0) to
estimate Vs30. The methodology used in this study is discussed in [18], in brief; first, we
computed the S-transform per component and obtained the geometric average of
horizontal components, latter, we smoothed both components, enabling the
computation of the horizontal over vertical spectral ratio. Finally, use the Table 2 of
reference [6] to associate a Vs30 value to each predominant period (T0), here instead of
using one Vs30 value for a range of T0 we linearly interpolate within the range.
1.3. Dataset
The dataset used in this study includes 988 records from 246 earthquakes. Figure 1
shows the relationship between moment magnitude and rupture distance. The dataset
has 725 records from 156 interface events, 19 records from 6 crustal events and 244
records from 84 inslab events. The magnitude range is Mw 5.0-8.8 and, closest distance
range is 25 to 650 kilometers.
Figure 1 Magnitude-distance distribution for Chilean seismic data.
2. Ground Motion Processing
All records have been processed component per component. The pre-processing
sequence is as follows; Analog instruments are instrument corrected [19], then the
record is classified as a late or normal triggered following Pacor et al. [20] proposal,
then spurious spikes are identified [21].
Next in cases were multiple shocks are present on the same record, only the main
shock is kept, including from pre-event noise to coda waves [22], the arrival times of P
and S-waves were picked by visual inspection and, S-waves end time was taken as the
time corresponding to 80% CAV of the record.
To select the low cut-off frequencies, for each component of each record, we
smoothed the S-wave and noise Fourier spectra using Konno-Ohmachi [23] with b-
parameter equal a 20. Then divided the spectrums to obtain Signal Noise Ratio (SNR)
and pick the low cut-off frequency with a SNR greater than 3. The high cut-off
frequencies were selected first to be the Nyquist frequency (as an initial estimation),
and then check for the flat part of spectrum [24], selecting the lower of those.
Finally, the processing is terminated following a methodology adapted from [25],
which consists in a taper the beginning and end of trace (if it is a late triggered record,
only taper end), zero padding, applying a 4-pole Butterworth filter with the corner
frequencies obtained in the previous analysis. Then, we obtain displacements and
correct baseline (fitting a sixth-order polynomial) and last, subtract the second derivate
of polynomial from acceleration, obtaining the corrected traces of acceleration.
3. GMPEs Evaluation: Goodness-of-Fit of GMPEs to Observed Data
To assess the Goodness-of-Fit (GOF) of the different GMPEs to the data, an analysis of
the residuals is necessary. We use two different approaches, study how the observed
data fit the GMPEs using the likelihood (LH) concept as described by [7], and average
sample log-likelihood (LLH) values [8].
Table 1 shows the GMPEs used in this study, Ruiz & Saragoni [2] was excluded of
the final analysis because the model does not have a published standard deviation and
uses Ms instead of the accepted Mw [26], making the comparison impossible with this
scheme.
Table 1. Summary table of GMPEs for subduction zone relevant in Chile
GMPE Region[a] Intensity
Measures N° records /
N° events[e] Mag Distance[f] Site
Characterization
AB03[3] AL, CS, CH,
JA, ME, PE PGA,
PSA Iter = 349/49
Itra = 761/30 Mw Rrup 4 discrete class
(NEHRP B, C, D, E)
RS05[2] CH PGA Iter=49/8
Itra=19/7 Ms Rhyp Hard rock and
rock/hard soil[c]
ZH06[6] JA PGA, SA Iter=1508
Itra=1725 Mw Rrup 5 discrete class (hard
rock + 4 soil class)
BC12[5][b] CH PGA,
PSA Iter=117/13 Mw Rrup 2 discrete class (rock
and soil)[d]
BCHy10[1] JA, TW, CD,
ME, PE, CH,
AL, SI. PGA, SA Iter=1378/46
Itra=3946/76 Mw Rrup (iter) /
Rhyp (itra) Continuous (Vs30)
[a]
AL=Alaska, SI=Salomon Island, CH=Chile, CS=Cascadia, JA=Japan, ME=Mexico, PE=Peru, TW=Taiwan
[b]
Only predicts interface event
[c]
Hard rock is defined by V
s30
≥1500 (m/s) and rock/hard soil by 360≤V
s30
[d]
Rock are defined by V
s30
≥900 (m/s) and in other cases the sites are classified as soil.
[e]
Iter, interplate events. Itra, intraplate/inslab events
[f]
R
rup
, closest distance to the fault plane. R
hyp
, hypocentral distance.
The normalized residuals are defined as the log of the observed seismic intensity
(IMobs) minus the log of the predicted seismic intensity (IMGMPE) divided by the total
standard deviation of model in natural log units (Eq. 1).
=()()
(1)
The evaluation was made with native periods of each GMPE (i.e. no interpolation
was performed). The residuals were computed for PGA and pseudo-spectral
accelerations of 0.1, 0.4, 1.0 and 2 seconds. The objective of this methodology is to
characterize the GOF of each GMPE to three parameters of central tendency (i.e. mean,
median, and standard deviation of the normalized residual distribution), and LH values.
If the data is unbiased, the normalized residuals would be distributed with zero mean
and unit variance. The classes which define the GOF of each GMPE to the observed
data are defined in [7] and consist in four levels of predictive capability; class A is for
models with high predictive capability, class B with intermediate capability, class C
with low and D with unacceptable predictive capability.
Moreover, we computed the average sample LLH values according the
methodology proposed by Scherbaum [8]. For in depth statistical methodologies see
references [7] and [8].
The analysis of GOF was segregated by interface and inslab events. Figure 3 and
Table 2 show the result of the analyses for interface events, while Figure 2 and Table 3
show the results for the inslab events. Median LH values close to 0.5 imply good
agreement between observed and predicted surface intensities. For the LLH values,
lower values imply a better prediction capacity of each model, in this case values lower
than 1.9 are considered as acceptable. Note that for the inslab events the BC12 GMPE
could not be assessed as they do not provide a model for these events.
Figure 2. GOF analysis of GMPE for pseudo spectral acceleration at 0.01 (PGA) s and 1 s, considering only
inslab events. First and third column are distributions of normalized total residuals, the solid red line is the
normal distribution fitted to normalized total residuals, the green dashed lines are the associated standard
normal distribution. Second and last column are associated likelihood values (LH).
0 0.5 1
0
50
100 Class D
Median: 0.15
-5 0 5
0
0.2
0.4
0.6
Mean: 1.35 | std: 1.31
Freq
AB03 0.01sec
0 0.5 1
0
50
100
150 Class D
Median: 0.13
-5 0 5
0
0.2
0.4
0.6
Mean: 1.38 | std: 1.51
AB03 1 sec
0 0.5 1
0
20
40 Class B
Median: 0.47
-5 0 5
0
0.2
0.4
0.6
Mean: 0.32 | std: 0.98
Freq
ZH06 0.01sec
0 0.5 1
0
50
100 Class D
Median: 0.31
-5 0 5
0
0.2
0.4
0.6
Mean: -1.01 | std: 1.27
ZH06 1 se c
0 0.5 1
0
20
40
LH
Class C
Median: 0.44
-5 0 5
0
0.2
0.4
0.6
Mean: 0.63 | std: 0.95
Zt
Freq
BCHy10 0.01sec
0 0.5 1
0
20
40
LH
Class B
Median: 0.44
-5 0 5
0
0.2
0.4
0.6
Mean: -0.41 | std: 1.22
Zt
BCHy10 1 se c
Figure 3. GOF analysis of GMPE for pseudo spectral acceleration at 0.01 (PGA) s and 1 s, considering only
interface events. First and third column are distributions of normalized total residuals, the solid red line is the
normal distribution fitted to normalized total residuals, the green dashed lines are the associated standard
normal distribution. Second and last column are associated likelihood values (LH).
Table 2. Classes and average sample LLH values of GMPEs used in analysis for interface events.
Class (LLH Value)
GMPE Sa @ 0.01 s
/ PGA
Sa @ 0.1 s Sa @ 0.4 s Sa @ 1 s Sa @ 2 s
AB03 D (10.3485) D (10.6382) D (8.2502) D (3.5442) C (2.1331)
ZH06 A (1.6493) A (1.7635) C (2.0175) D (2.4347) D (2.5057)
BCHy10 D (2.5594) D (2.8881) D (2.6357) B (1.8701) B (1.8415)
BC12 C (1.9526) C (1.7696) C (2.2563) D (3.0964) D (3.5439)
Table 3. Classes and average sample LLH values of GMPEs used in analysis for inslab events.
Class (LLH Value)
GMPE Sa @ 0.01 s
/ PGA
Sa @ 0.1 s Sa @ 0.4 s Sa @ 1 s Sa @ 2 s
AB03 D (3.1745) D (4.5309) D (5.8017) D (3.7637) D (2.5492)
ZH06 B (1.5474) D (2.0568) D (2.4405) D (2.7359) D (2.7763)
BCHy10
C (1.8807)
D (2.2213)
A (1.8468)
B (2.1353)
B (2.0818)
0 0.5 1
0
200
400
600 Class D
Median: 0
-5 0 5
0
0.2
0.4
0.6
Mean: 2.83 | std: 2.41
Freq
AB03 0.01sec
0 0.5 1
0
100
200
300 Class D
Median: 0.19
-5 0 5
0
0.2
0.4
0.6
Mean: 1.23 | std: 1.44
AB03 1 sec
0 0.5 1
0
50
100 Class A
Median: 0.45
-5 0 5
0
0.2
0.4
0.6
Mean: 0.02 | std: 1.11
Freq
ZH06 0.01sec
0 0.5 1
0
100
200
300 Class D
Median: 0.27
-5 0 5
0
0.2
0.4
0.6
Mean: -0.97 | std: 1.11
ZH06 1 se c
0 0.5 1
0
100
200 Class D
Median: 0.28
-5 0 5
0
0.2
0.4
0.6
Mean: 1.02 | std: 1.09
Freq
BCHy10 0.01sec
0 0.5 1
0
50
100
150 Class B
Median: 0.47
-5 0 5
0
0.2
0.4
0.6
Mean: 0 | std: 1.13
BCHy10 1 se c
0 0.5 1
0
100
200
LH
Class C
Median: 0.31
-5 0 5
0
0.2
0.4
0.6
Mean: -0.33 | std: 1.48
Zt
Freq
BC12 0.01sec
0 0.5 1
0
200
400
LH
Class D
Median: 0.17
-5 0 5
0
0.2
0.4
0.6
Mean: -1.06 | std: 1.6
Zt
BC12 1 sec
4. Conclusions
A rigorous assessment of different GMPEs developed for subduction environments was
performed. The data used to compare these GMPEs was also rigorously processed; it
includes analog and digital records from 1985 to 2014, for inslab and interface events.
These interface events include the largest ground motions recorded in Chile.
Of the initial set of selected GMPEs to be analyzed, RS05 [2] had to be excluded
from the analyses as the lack of standard deviation of the models does not allow the
comparison used in this work.
Our results indicate that BCHydro [1] has similar behavior to Zhao et al. [6] for
interface (or interplate) events. For short periods, ZH06 et al. shows the best fit to the
data (LLH<1.8), and for the longer periods (Sa at 1 and 2 seconds) BCHydro is the best
fit(LLH<1.9). BC12 shows less accuracy (class C in Scherbaum’s [7] methodology,
average LLH of 2.5) and obtains “low capability” level in some periods (PGA, 0.1, and
0.4 s), with good LLH value for Sa at 0.1 secs. AB03 obtain “unacceptable capability”
(class D along with high LLH values) for all periods except in pseudo spectral
acceleration at 2 seconds.
Inslab ground motion intensities show a similar behavior to those of interface.
Again BChydro and ZH06 are the best performing GMPEs for the Chilean data. For
these events BCHydro performs better for Sa at 0.4, 1, and 2 seconds, while ZH06 does
better at PGA and 0.1 secs. As mentioned above, the BC12 could not be used. AB03
did not fit well this data either.
Contrary to the expected, BC12 did not perform better than the other models. Good
behavior was expected because the model was fitted to Chilean data, much of the same
data that was used to tests all GMPEs. Improvements in the statistical processing of the
data, will likely lead to lower standard deviation estimations of this model and thus an
overall improvement of its performance.
As shown in Figures 2 and 3, the analyzed GMPEs have predominantly positive
residuals, underestimating pseudo accelerations at the periods under consideration. An
exception to this trend is the BC12 GMPE which overestimates Sa values at all periods.
Acknowledgements
This work was partially funded by FONDECYT 11121404, is part of an ongoing effort
by GEM-SARA (Global Earthquake Model - Seismic Risk in South America) topic 6
workgroup, and FUCHIGE foundation. The data of shear wave profiles of Chilean
stations are available at FUCHIGE´s website.
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2010), 81(5):783-793
... El método de la log-verosimilitud promedio de Scherbaum et al. (2009) es usado para cuantificar la bondad del ajuste entre los valores predichos por las leyes de atenuación y la base de datos. Este método ha sido aplicado exitosamente por estudios previos (Bastías et al. 2015;Beauval et al. 2012; Ogweno y Cramer 2014, entre otros). ...
... Valores pequeños de LLH indican mejor capacidad predictiva de la ley de atenuación, mientras que valores grandes de LLH indican que es menos probable que el modelo de atenuación haya generado los datos observados. Así pues, Bastías et al. (2015), sugiere un límite superior de 1.9 para aceptar un modelo de atenuación. Asimismo, a fin de visualizar el ajuste entre los datos y las predicciones de las leyes de atenuación se muestra la distribución de los residuos normalizados. ...
... Sin embargo, al dividir el análisis según la magnitud de los sismos (Figura 3b,c) se observa que este sesgo de debe a sismos de magnitudes entre 5.0 y 6.0 Mw lo cual es coherente con las restricciones del modelo (ver Tabla 1), puesto que el modelo BC2016 fue realizado para sismos de magnitudes mayores o iguales a 6.0 Mw. El desajuste del modelo BC2016 para periodos menores a 1.0 s también fue observado por Arango et al. (2012) y Bastías et al. (2015); sin embargo, el presente estudio resalta que este sesgo para periodos menores a 1.0 s se debería a que el modelo excluye en su desarrollo sismos de subducción interfase con magnitudes menores que 6.0 MW. Por otro lado, en el caso de sismos de subducción intraplaca el modelo BC2016 presenta el mejor ajuste de todos los modelos analizados, con valores LLH menores que 1.9 para todo el rango de periodos. ...
Conference Paper
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RESUMEN: La evaluación del peligro sísmico es una herramienta importante para la mitigación de desastres en el Perú. En la evaluación del peligro sísmico, el factor que introduce mayor variabilidad en los resultados finales es la selección de la ley de atenuación utilizada en el análisis. Existen dos leyes de atenuación derivadas de registros sísmicos peruanos, sin embargo, estas presentan limitaciones importantes por la escaza cantidad de registros sísmicos. Este trabajo investiga la aplicabilidad de leyes de atenuación modernas derivadas de datos globales y regionales a la zona de subducción del Perú. Para este fin, se compila una base de datos de 485 registros sísmicos de 118 sismos de subducción registrados en el Perú y el norte de Chile, ocurridos entre 1966 y 2015 con magnitudes que varían entre 5.0 a 8.4 Mw. La evaluación del ajuste de las leyes de atenuación a los datos compilados se realiza mediante el método LLH (log-verosimilitud promedio) propuesto por Scherbaum et al. (2009). En el Perú, los resultados obtenidos indican que para sismos de interfase los modelos de Youngs et al. (1997) y Zhao et al. (2016b) son los que mejor ajuste presentan para todo el rango de periodos, mientras que el modelo de Abrahamson et al. (2016) presenta un buen desempeño para periodos mayores a 1.0 s, sin embargo, no sería aplicable para la costa norte del Perú. En el caso de sismos de intraplaca, el modelo de Abrahamson et al. (2016) muestra el mejor ajuste a los datos compilados, seguido del modelo de Zhao et al. (2016a). Con base en estos resultados, se ha estimado coeficientes de ponderación para las leyes de atenuación seleccionadas en perfiles de suelo tipo B y C (IBC, 2015). ABSTRACT: The seismic hazard assessment is an important tool for disaster mitigation in Peru. The selection of ground motion prediction equations (GMPEs) tends to exert a greater influence on the final results of seismic hazard calculations. Currently, in Peru two GMPEs exists that are derived from local data, however, these models have important shortcomings due to few records used. This study, investigates the applicability of foreign GMPEs derived from regional and global data to the Peruvian subduction-zone. To that end, it was compiled a database of subduction-zone strong-motion records, which consists of 485 ground-motion records from 118 subduction-type events with moment magnitudes ranging from 5.0 to 8.4, recorded in Peru and northern Chile, between 1966 and 2015. The average "log-likelihood" method (LLH) of Scherbaum et al. (2009) has been applied to assess the goodness of fit of different GMPEs to the data. Results show that for interface models Youngs et al. (1997) and Zhao et al. (2016b) have the best performance, meanwhile the Abrahamson et al. (2016) model shows a good fit for periods greater than 1.0 s, but it would not be applicable for the Peruvian northern coast. For intraslab events, the Abrahamson et al. (2016) model shows the best fit to the data followed by the Zhao et al. (2016a) model. Finally, weighting coefficients for each selected GMPE were proposed for B and C site classes (IBC, 2015). PALABRAS CLAVES: Leyes de atenuación; peligro sísmico en el Perú; sismos de subducción
... With the growing strong-motion data set available for Chile, several studies have evaluated the GMM predictive performance in the country. Bastías et al. (2015) evaluated four GMMs with a nationwide ground-motion data set. Two of these models were derived using global data Boore, 2003, 2008;Abrahamson et al., 2016), one was derived using only Chilean data (Contreras and Boroschek, 2012), and one was derived using Japanese data (Zhao et al., 2006). ...
... Two of these models were derived using global data Boore, 2003, 2008;Abrahamson et al., 2016), one was derived using only Chilean data (Contreras and Boroschek, 2012), and one was derived using Japanese data (Zhao et al., 2006). From those five models, Bastías et al. (2015) found that the BC Hydro GMM (Abrahamson et al., 2016) shows the best fit to observed ground motions at long periods, whereas Zhao et al. (2006) shows the best fit at short periods. Piña-Valdés, Socquet, Cotton, and Specht (2018) carried out a ground-motion evaluation in northern Chile, taking advantage of the data provided by a permanent seismic network deployed on rock since 2006. ...
Article
Strong-motion observations of recent interface earthquakes along the Chilean subduction zone are evaluated with two ground-motion models (GMM). One GMM was developed with Chilean data and the other with global data. The GMM developed with local Chilean data is found to have an overall better prediction performance than the GMM developed using a global data set. Using residual analysis with the Chilean GMM as reference model due to its better performance, clear indications of an increase of short-period radiation for deeper earthquakes in north and central Chile were found, which may be related to frictional features on the interface such as interseismic coupling, as found previously for other regions, such as Japan. Also, the Iquique earthquake, which featured a clear precursory slow-slip event, exhibits mostly negative between-event residuals at short periods for earthquakes before and after the mainshock, indicating predominantly weaker short-period radiation. However, this trend is not observed in the aftershock sequence of the Illapel earthquake, which did not feature a significant slow-slip event nor precursory seismicity in its rupture area. Finally, a poor predictive performance was found for the Chilean GMM in southern Chile, overpredicting most of the observations. Based on these results, it is proposed that future local GMMs should include corrections for depth, regional effects and include earthquakes from southern Chile, as new data are becoming available in this region.
... One of the 28 main tasks in seismic hazard assessment is the selection of appropriate Ground Motion Predictive Models (GMPMs) to 29 be used. Data-driven evaluation of GMPMs has become possible thanks to the increasing amount of available strong-30 motion data and can give valuable information regarding the ability of empirical models to predict ground motion in 31 various regions (Drouet et al., 2007;Allen and Wald, 2009;Delavaud et al., 2012a and b;Bastias et al., 2015;Zafarani 32 and Farhadi, 2017;Lanzano et al., 2020). 33 ...
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The aim of the present work is to evaluate the Ground Motion Prediction Models (GMPMs), applicable in active shallow crustal regions of Greece, for peak response parameters (PGA, PGV) and various ordinates of acceleration spectra (5% damping), to be implemented in Probabilistic Seismic Hazard Analyses (PSHA). The evaluation is data-driven, taking into account the most updated strong-motion dataset for Greece. According to the authors’ knowledge and literature review, such an effort has not been attempted before for region of interest, as the selection of GMPMs for PSHA was made based on qualitative criteria. The steps which were followed to fulfill such a goal include: a) pre-selection of regional, pan-European and global GMPMs, suitable for implementation in Greece; b) selection of appropriate data-driven scoring methods; c) scoring of pre-selected GMPMs for both peak response intensity measures and spectral acceleration values; d) final selection of GMPM suites and weighting. The final selection of GMPMs describes adequately the epistemic uncertainty of strong-motion in Greece and provides a useful tool for addressing a major component of PSHA studies, that is GMPM selection.
... θ 7 , θ 8 , θ 15 and θ 16 ) of the original model were fixed to zero because not contribute to predict a ground motion intensity. The selection of the functional form from [9] is due to the good fit to a subset of the data from the Chilean subduction zone used in this study ( [10]). This functional form has theoretical advantages over others because it includes non-linear site response. ...
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Single station sigma allows for a more realistic estimation of expected seismic demand. The use of single-station sigma in a non-ergodic Probabilistic Seismic Hazard Analsyis framework avoids the double counting of uncertainties and allows for the more rigorous incorporation of site and path effects into hazard estimations. We present a ground motion prediction model (GMPE) developed from a catalogue that includes Chilean interplate and inslab events. The model is used to evaluate the quantity of uncertainty that can be attributed to source, path, site effects, and what can be treated as pure aleatory uncertainty. Our results show remarkable agreement with other studies in some components of the overall uncertainty, but differences in other components, which shed light into the areas that could be advanced in the future. Site characterization and prediction of Vs30 through proxies represent a key challenge and where leap improvement can be achieved.
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A ground‐motion prediction equation for the horizontal component of the response spectral values from the Chilean subduction zone is developed. The dataset contains 3774 recordings from 473 earthquakes, including the latest megathrust events that occurred in the country (i.e., 2010 Mw 8.8 Maule, 2014 Mw 8.1 Iquique, and 2015 Mw 8.3 Illapel). The functional form for the median model follows the proposal by Abrahamson et al. (2016). Site effects are estimated based on VS30. An additional model is built using only ground motions associated with measured values of VS30 and reliable Mw estimates (termed the high‐quality [HQ] model). The standard deviation of the HQ model is much lower than the main model, particularly the component of the standard deviation that corresponds to site‐to‐site variability, indicating that better site characterization can significantly reduce the overall uncertainty in ground‐motion estimation.
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La demanda sísmica superficial está fuertemente controlada por las condiciones locales del sitio en estudio. Esto ha quedado en evidencia en numerosos terremotos como Loma Prieta 1989, México 1985, o Maule 2010, por nombrar algunos; por este motivo que en la actualidad, la caracterización geofísica del sitio en estudio es un paso obligatorio en cualquier estimación de demanda sísmica (i.e., estudio de peligro sísmico). La estimación de la demanda sísmica superficial es preferiblemente realizada utilizando métodos probabilistas por constituir la única manera racional de abordar la enorme incertidumbre asociada al fenómeno, sin embargo, complementos mediante análisis determinísticos son recomendables dependiendo de la situación. En este trabajo se explora el impacto que tiene la caracterización geotécnica y geofísica del sitio en estudio, para una correcta estimación de la demanda asociada a distintos periodos de retorno. Finalmente, se discute sobre la importancia de una adecuada campaña de exploración y la pertinencia de que estudios de este tipo sean realizados por profesionales competentes no sólo en análisis probabilista sino en ingeniería geotécnica y particularmente en dinámica de suelos.
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https://nisee.berkeley.edu/elibrary/Text/201405216 Presents one-dimensional shear-wave velocity (VS) profiles at 31 strong-motion sites in Chile, from Valdivia in southern Chile to Copiapo in the northern Atacama Desert. We estimate the VS profiles with the spectral analysis of surface waves (SASW) method. The SASW method is a non-invasive method that is useful for indirect estimate of the VS at depth from variations in the Rayleigh wave phase velocity at the surface. The purpose of the study is to determine the detailed site velocity profile, the average velocity in the upper 30 m of the profile VS30, the average velocity for the entire profile, VS,z, and the NEHRP site classification.
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The Cedar Hills Nursery strong motion record of the 1987 October 1st Whittier Narrows earthquake can be decomposed into five physically distinct components. These are the usual radial, circumferential and vertical components, plus a 2.84 Hz resonance along 317 °N and a 3.27 Hz resonance along 317 °N. The significance and possible cause of the resonant parts are discussed.
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The Mw 8.8 Maule Chile earthquake is one of the largest magnitude events to have produced strong motion recordings world-wide. In this paper we describe attributes of the recording stations, the data processing procedures and ground motion intensity measures computed from the records. We then compare spectral accelerations to predictions from GMPEs. Finally we present preliminary attenuation relations for horizontal spectral accelerations developed using a database of Chilean accelerograms recorded during interface earthquakes occurred between 1985 and 2010.
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This report summarizes the products and results of a study on the collection, processing, and analysis of earthquake ground-motions recorded in Arizona at several recording stations within 200 km from the Palo Verde Nuclear Generating Station in central Arizona. The recorded ground motion in Arizona were compiled and processed according to the Pacific Earthquake Engineering Research Center’s (PEER) record-processing standards. Shear-wave velocity profiles at ten recording stations were measured through the spectral analysis of surface wave dispersion technique. Additionally, “kappa” a measure of energy dissipation in the top 1 to 2 km of the crust, was estimated by three methodologies. The average κ0 (kappa at zero-kilometer distance) was estimated from all sites as 0.033 sec. Finally, response spectra of the recorded ground motions in Arizona were compared with those predicted by the NGA-West2 ground motion prediction equations at large distances in Arizona. The comparison showed that overall the recorded 5% damped response spectral ordinates were over predicted by the NGA-West2 models by a range of 00.35 natural log units for events occurring in Central California, and by a range of 0.20.7 natural log units for events occurring in Southern California and the Gulf of California.
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An updated ground motion prediction equation (GMPE) for the horizontal component response spectral values from subduction zone earthquakes is developed using a global data set that includes 2,590 recordings from 63 slab earthquakes (5.0 ≤ M ≤ 7.9) and 953 recordings from 43 interface earthquakes (6.0 ≤ M ≤ 8.4) at distances up to 300 km. The empirical data constrain the moment magnitude scaling up to M8.0. For M > 8.0, a break in magnitude scaling is included in the model based on the magnitude scaling found in numerical simulations for interface earthquakes in Cascadia. The focal depth scaling of the short-period spectral values are strong for slab earthquakes, but it is not seen for interface events. The distance scaling is different for sites located in the forearc and backarc regions, with much steeper attenuation for backarc sites. The site is classified by VS30 with constrained nonlinear site amplification effects.
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The spectral ratio between horizontal and vertical components (H/V ra-tio) of microtremors measured at the ground surface has been used to estimate fun-damental periods and amplification factors of a site, although this technique lacks theoretical background. The aim of this article is to formulate the H/V technique in terms of the characteristics of Rayleigh and Love waves, and to contribute to improve the technique. The improvement includes use of not only peaks but also troughs in the H/V ratio for reliable estimation of the period and use of a newly proposed smoothing function for better estimation of the amplification factor. The formulation leads to a simple formula for the amplification factor expressed with the H/V ratio. With microtremor data measured at 546 junior high schools in 23 wards of Tokyo, the improved technique is applied to mapping site periods and amplification factors in the area.
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Most strong-motion data processing involves acausal low-cut filtering, which requires the addition of sometimes lengthy zero pads to the data. These padded sections are commonly removed by organizations supplying data, but this can lead to incompatibilities in measures of ground motion derived in the usual way from the padded and the pad-stripped data. One way around this is to use the correct initial conditions in the pad-stripped time series when computing displacements, velocities, and linear oscillator response. Another way of ensuring compatibility is to use post-processing of the pad-stripped acceleration time series. Using 4071 horizontal and vertical acceleration time series from the Turkish strong-motion database, we show that the procedures used by two organizations—ITACA (ITalian ACcelerometric Archive) and PEER NGA (Pacific Earthquake Engineering Research Center–Next Generation Attenuation)—lead to little bias and distortion of derived seismic-intensity measures.