ACCURACY OF SOME GROUND MOTIONPREDICTION MODELS FOR PPI STATION-WEST
SUMATRA USING OBSERVED STRONG MOTIONDATAHendarto∗
MEE07152

Supervisor:

Tsuyoshi TAKADA∗∗ABSTRACTThis study proposes the ground motion prediction models applicable to PPI Station located in Padang
Panjang, West Sumatra, at which 120 components of strong motion data of 40 earthquake events withsampling rate 100 Hz have been recorded, in order to find site-specific parameters used to investigate
the site class and the accuracy of the prediction models for this station. The H/V spectral acceleration
ratio scheme is adopted to find the dominant period of ground beneath this station since there are noquantitative subsurface soil properties available. The results of H/V analysis show that the mean peakof H/V ratio of EW, NS and total horizontal components to vertical component is around 0.2 second.
The peak period of H/V ratio is in good agreement with the geologic information from geologicalsurface map published by Geological Research and Development Center (GRDC, 1973). The geologic
formation for the ground beneath and around this station is dominated by quaternary volcanic rock(Qast). Both results indicating the site class for the ground beneath the station could be categorized as
rock site with AVS30 more than 600 m/s. The existing prediction models for peak ground acceleration(PGA) and 5% damped spectral acceleration (Sa) at rock site are tested in this study. The accuracy ofthe selected models is discussed on the basis of the statistical distribution of the logarithmic deviation
between the prediction and the observed value. The mean residuals of all prediction models of Sa arefound to have the same tendency that they underestimate Sa at period T = 0.2 second and overestimate
Sa at T ≥ 0.4 second. It is found that all prediction models show significant period-dependent meanerrors and are not allowed to be applied to this site without site correction factors, and among them,
Youngs et al., 1997, Kanno et al., 2006, and Zhao et al., 2006 provide the smallest prediction error.
Based on these results, it can be concluded that all prediction models may be applied with each site
correction factor to reflect the site-specific condition and among them three models proposed byYoungs et al., 1997, Kanno et al., 2006, and Zhao et al., 2006a are the best for this station. However,

the error is still large; therefore a further study is needed to check it.
Keywords: peak ground acceleration, 5% damped spectral acceleration, mean residuals.
INTRODUCTION
One of the important factors in the seismic hazard assessment for certain regions is the selection ofground motion prediction model which is also known as attenuation relation model.
There are many attenuation relation model determined by the different definitions ofindependent variables such as distance, magnitude, site condition, types of faulting and selection of thehorizontal ground motion component. Unfortunately, there is no available attenuation model derived∗

State Ministry of Research and Technology, Indonesia.
Professor, Graduate School of Engineering, the University of Tokyo, Japan.

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from using strong motion databases from Indonesia despite Indonesia is located at active tectonicregion.
We adopt only some attenuation relation models in both probabilistic and deterministic
seismic hazard calculation. However, the selection of suitable attenuation relation for a certain area
often causes serious practical problem and doubt since there are more than 206 attenuation relation
models to predict PGA and 127 attenuation models to predict response spectra ordinate (Douglas,2006).
This study will emphasize the scheme in selecting suitable attenuation relation models fromsome model candidates and also find the site factor for a particular site to account the local sitecharacteristic of the site based on the selected attenuation relation model. Site factor is the mean valueof the logarithmic residuals between the observed and predicted values, and evaluated it with thelimited observations (Morikawa et al., 2006).

DATA
120 components of qualitative records with sampling rate 100 Hz from 40 events at PPI station used inthis study are provided by JISNET-NIED with permission from Meteorological and GeophysicalAgency of Republic of Indonesia (BMG Indonesia).
The basic assumptions of earthquake source parameters such as epicenter location, focal depth,
magnitude and focal mechanism are referred to CMT and PDE-USGS catalog. Most events, 33 of 40
events are referred to CMT catalog, while the information of other 7 events is referred to PDE-USGS
catalog.
39 of 40 events were triggered by subduction earthquake sources. Another one event wastriggered by shallow crustal source.

THEORY AND METHODOLOGYMagnitude Scale
It is an important consideration to use a unified magnitude scale in attenuation study. Therefore in thisstudy, the relation of mb, MS and MW is adopted from Scordilis, 2006 in order to unite magnitude scale.
By applying these equations, the homogeneity of database will be provided.
Relation between MW and MS, for 3.0 ≤ MS ≤ 6.1

M W = 0.67(±0.005)M S + 2.07( ±0.03)(1)Relation between MW and mb, for 3.5 ≤ mb ≤ 6.2

M W = 0.85(±0.04)m b + 1.03(±0.23)(2)

Attenuation Relation Models

We select five models for spectral acceleration (Sa) since we cannot cover all existing models in thisstudy. The five models for predicting Sa are Atkinson and Boore, 1997 (AB97); Kanno et al., 2006
(K06-S and K06-D); Midorikawa and Uchiyama, 2006 (MU06-S and MU06-D); Youngs et al., 1997
(Y97-Inter and Y97-Intra); and Zhao et al., 2006a (Z06).
The selected period range for this study is from PGA to T ≤ 2 second based on the mostbuilding types, geometries and structures constructed around the particular site (i.e. PPI seismicstation) which are dominated by concrete structure buildings with the total number of stories vary from
1 to 8-story buildings.

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Site Classification Method

There are some previous studies conducted by several researches related to the determination of siteclassification with different methods. Two parameters that commonly used in those studies are theaverage shear wave velocity of top 30 m (AVS30, V30 or ) and the dominant period of site (TG). Therelation between them is described in many codes such as NEHRP and Japan Road Association, 1980.
Zhao et al., 2006b used V30 (using 0.25 times the site period in four site classes as the shear
travel time in the top 30 m soil layers, see Eq. (3)) and site classes used in the Japan Road Association,
1980 and NEHRP Provision (see Table 1).
In this study, H/V response spectral ratio scheme proposed by Zhao et al., 2006 will beadopted to find the dominant period (TG) of site of PPI seismic station located. The result of H/Vscheme will be cross-checked by surface geology around PPI Station based on geology maps ofPadang provided by Geological Research and Development Center (GRDC, 1973), Republic ofIndonesia.
V30 =30
0.25.TG(3)

Table 1. Site class definitions used in Japan for engineering design practice and the approximately
corresponding NEHRP site classes (Zhao et al, 2006b)

Site class
SC ISC IISC IIISC IV

Rock/stiff soil
Hard soil
Medium soil
Soft soil

Site natural period(sec)
TG < 0.2
0.2 ≤ TG < 0.40.4 ≤ TG < 0.6TG ≥ 0.6

Average V30(m/s)
V30 > 600
300 < V30 ≤ 600
200 < V30 ≤ 300
V30 ≤ 200

NEHRP
classA+BCDERESULTS AND DISCUSSION
Site Class

Based on the H/V analysis result, the mean peakratio of EW and NS components to verticalcomponents is around 0.195 and 0.205,
respectively. Figure 1 shows the mean peakratio of total components to verticalcomponents. By referring to Table 1, the siteclass beneath PPI Station could be SC I (class Aor B) or SCII (class C).
As mentioned earlier, another way as basic
engineering judgment to determine site class isthat by using the surface geological condition ofsite beneath and around PPI Station.
Figure 1. The mean H/V ratio of spectral
acceleration at PPI station.

3Figure 2 shows that the structural geology
beneath and around this station is dominated byvolcanic rocks (andesit rock, Qast). By referringto NEHRP classification, we may make apreliminary estimate that the site class beneath
Gthis station is probably class C (very dense/softFrock) or B (rock). Based on the H/V spectral

acceleration ratio and the geological surface
condition, the site classification of PPIstation can be assumed as rock site (SC I orwith the average shear wave velocity of top
30m is predicted more than 600 m/s.

EFigure 2. Geology map around PPI station (GRDC,1973).
Selecting Candidates

The critical question arisen is how to select appropriate attenuation model for a particular site, i.e. PPIStation. We did statistical analysis for answering that question based on the mean residuals. There areseveral different ways that can be used to construe the results of the statistical analysis. The first
method is to select the best model by using both the lowest mean residual (see Figure 3) and standard
deviation (Figure 4) at the required and specific period. The second method is to select two or threesatisfactory models and average the predicted ground motions. The last method is to adjust all
prediction models by applying a correction factor based on the mean residuals.

Figure 3. Mean residuals of Sa for all attenuation
relation models.

Figure 4. Uncertainty of Sa for all attenuation
relation models.

Generally, the decision in selecting the best model will be depended on the application ofinterest. Related to the first method, some findings can be used as guidance to justify which one is themost appropriate among the used models in this study. The important thing based on the mean
residuals (see Figure 3) is that all models have the same tendency which underestimated to predict Sa
at period T = 0.2 second and overestimated to predict Sa at T ≥ 0.4 second. At the short period range,Zhao et al., 2006 and Kanno et al. 2006 attenuation relation models provided smaller mean residuals
for PGA and T < 0.1 second (relatively), than the other models did; and Youngs et al., 1997 for bothinterface and intraslab events obtained the smallest mean residuals among others models for period
0.15 ≤ T < 0.25.

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Another important finding is that the comparison of uncertainties presented in Figure 4 forvarious period ranges. It can be concluded that at the short period, for PGA and T < 0.2, Youngs et al,.
1997 attenuation relation model for interface events has the smallest uncertainty value while Kanno etal., 2006 attenuation relation model for shallow events has the smallest one for period T ≥ 0.2 second.
We may say that Youngs et al., 1997, Kanno et al., 2006 and Zhao et al., 2006 are suitable for thisstation. Note that 39 of 40 events used in this analysis were triggered by subduction sources, hence wecould not say too much about the prediction of ground motions for crustal event since only one event
is available in the data base.
Site Correction Factor Based on Mean Residuals

The site correction factors can be defined as the factors determined by the average residuals treating
the adjusted attenuation relation model as a site-specific model. The relation between the original andthe adjusted model can be expressed by Eq. (4).
ln y(t)* = ln y(t) + s(t)orlog y(t)* = log y(t) + s(t)(4)

Where y(t)* is the adjusted model, y(t) is the original model and s(t) is the site correction factor atperiod t. The site correction factors for all prediction models are represented in Figure 5.

Figure 5. Site correction factors for various attenuation relation models.

5CONCLUSIONS

Site correction factors for all models are determined based on the value of mean residuals. Note thatthese corrections are made only for mean predictions, not for uncertainty predictions. We can adjust
the predicted value from each model for PPI station by adding these correction factors into the basemodels of each attenuation relation model, (see Eq. (4)). Then all prediction models may be appliedwith each site correction factor to reflect the site-specific condition and among them three models
proposed by Youngs et al., 1997, Kanno et al., 2006, and Zhao et al., 2006a are the best for this station.
However, the error is still large; therefore a further study is needed to check it.

RECOMMENDATION

As future studies, estimation of site correction factors for other sites, development of possible methods
for reducing prediction errors which are still large, and nation-wide deployment procedure to develop
site-specific ground motion prediction models.

AKNOWLEDGEMENT

I would foremost like to thank Dr. Yoshimitsu Fukushima for our scientific relationship in thepreparation and data treatment; Dr. Fauzi from BMG Indonesia, Dr. Hiroshi Inoue from JISNET
Network and Dr. Winfried Hanka from GFZ-Potsdam for strong motion data collection. I also thank Dr.
Anthony Lomax and Dr. Toshihide Kashima for either direct or indirect discussion related toSEISGRAM2k and Viewave; Wessel, P. and Smith, W. H. F. for the GMT software; and my advisor,Dr. Shin Koyama, for giving his time in our fruitful discussion

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