RNUS Annual Report
2008 and 2009
Seismic Hazard Assessmentin Thailand

Edited by
Kawin Worakanchana, Pennung Warnitchai
and Kimiro Meguro
International Center for Urban Safety Engineering (ICUS)
Institute of Industrial Science
The University of Tokyo, Japan

ICUS Report No.40
November 2009

PREFACE

This report describes seismic hazard assessment in Thailand as a part of RNUS activities
in 2008 and 2009. This activity was under the research project: “Master Plan for Earthquake andBuilding Collapse Hazard Prevention and Mitigation in Thailand (Phase I)” which was initiatedand sponsored by the Department of Disaster Prevention and Mitigation (DDPM) under
Thailand’s Ministry of Interior. This project was carried out by the joint consulting team of
RNUS, the Asian Institute of Technology (AIT) and Panya Consultants Co. Ltd.
The objective of seismic hazard assessment is to develop the seismic hazard map which
is an essential tool for developing pro-active strategy for mitigating earthquake disaster. Thismap can be beneficial for mitigating earthquake disaster by providing proper value of earthquake
level for seismic designs of building and infrastructure. Moreover, it will be used to prepare
adequate resources such as the number of officers and equipments required for earthquake
disaster mitigation. It can be also used for specifying the earthquake insurance premium in eachregion of the country.

Kawin Worakanchana, Pennung Warnitchai
and Kimiro Meguro

CONTENTS

1. INTRODUCTION

1

2. EARTHQUAKE CATALOG

2

2.1 Compilation

2

2.2 Magnitude Conversion

3

2.3 Declustering

4

2.4 Catalog Completeness1111

3. MODELLING OF EARTHQUAKE SOURCES

17

3.1 Background seismicity model

17
17

3.2 Subduction zone model

21
21

3.3 crustal fault source model

23
23

4. ATTENUATION MODELS

35

5. PROBABILISTIC SEISMIC HAZARD ANALYSIS

48

5.1 Results

48
48

5.1.1 Seismic Hazard Maps

48
48

5.1.2 Seismic Hazard in Bangkok

54
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November, 2009

RNUS ANNUAL REPORT 2008 and 2009
SEISMIC HAZARD ASSESSMENT
IN THAILAND
KAWIN WORAKANCHANAInternational Center for Urban Safety Engineering
Institute of Industrial Science
The University of Tokyo, JAPANPENNUNG WARNITCHAI
School of Engineering and Technology
Asian Institute of Technology, THAILAND
KIMIRO MEGURO
International Center for Urban Safety Engineering
Institute of Industrial Science
The University of Tokyo, JAPAN

1. INTRODUCTION
This report describes the seismic hazard assessment in Thailand which is apart of the research project: “Master Plan for Earthquake and Building
Collapse Hazard Prevention and Mitigation in Thailand (Phase I)”. This
project was initiated and funded by the Department of Disaster Prevention
and Mitigation (DDPM) under the Thailand’s Ministry of Interior.. The
work has been carried out by the consulting team consisting of RNUS,
Asian Institute of Technology (AIT) and Panya Consultants Co. Ltd.
Tsunami generated from the 2004 Sumatra earthquake has brought
one of the most tragic disasters to Thai people. After the event, most Thai
people began to realize how destructive an earthquake can be. They also
perceive that large earthquakes tend to occur more frequently in Thailand
and its neighbouring countries. Moreover, geological studies indicate that
potential seismic sources can affect 61 provinces out of the total 76. DDPM
has foreseen the possibility of earthquake destruction; therefore it initiatedthe project to provide a pro-active strategy for mitigating future earthquake
disasters. This plan will be used as a supplement to the national disasterprevention and mitigation plan for a wide range of Thailand’s organizations
which include ministries, departments, districts and local government units,
private entities, foundations, non-profit and non-government organizations
to further varies develop an earthquake disaster managing system.

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November,
November, 20092009This report consists of five parts. The first part in this projectdescribes the study area and development of the earthquake catalog. Then,the second part contains information about the earthquake source models.
The third one discusses the results obtained from the seismic hazard studies.
The probabilistic seismic hazard (PSHA) map in this report has been
carried out using the most updated earthquake catalog until 2007. It is also
the first PSHA map for Thailand that includes the effect from faults. We
also adopted the spatially smoothed seismicity model (Frankel, 1995) toimprove the subjective judgment from delineation of areas used in the
traditional approach for model background seismicity.
The map will have the important role in the earthquake mitigation
plan in Thailand. It will provide the proper value of earthquake level forseismic design of buildings and infrastructure. Moreover, it will be used toprepare proper resources such as the number of officers and equipments
required for earthquake mitigation in each area in Thailand. It will be also
used for specifying the earthquake insurance premium.
2. EARTHQUAKE CATALOG
The earthquake catalog contains instrumental records of past earthquakes. It
is one of the most important tools used for interpreting seismicity anddeveloping a seismic hazard map. The earthquake catalog containing alonger period of earthquake data generally represents a more complete
image of seismicity and increases the accuracy of the seismic hazard map.
2.1 Compilation
The earthquake catalog in this project was originated from 4-year research,data compilation, and interpretation efforts of the Southeast Asia
Association of Seismology and Earthquake Engineering (Nutalaya, 1985).
The catalog contains the instrumental data of earthquake from 1912 to 1983
within boundary of 5oN to 25oN and 90oE to 110oE. The instrumental
record was compiled from several sources, e.g. International Seismological
Summary (ISS) (1914-1963), International Seismological Center (ISC)(1964-1980), Thai Meteorological Department (TMD) (1976-1983) and U.S.
Geological Survey (USGS) (1975-1982).
Under a research project entitled “Assessment and Mitigation ofEarthquake Risk in Thailand (Phase I)”, the earthquake catalog was
extended by adding the instrumental records from TMD (1984-2002) within
0oN to 30oN and 88oE to 110oE.
In this project, we updated the catalog by adding the instrumental records
from 2003 to 2007 from TMD and USGS/NEIC (National Earthquake
Information Center) Preliminary Determination of Epicenters on-line
catalogue (http://neic.usgs.gov). The earthquake data from 1977 to 2007

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from on-line USGS scientific data were also added to the catalog(http://earthquake .usgs.gov/research/topic). The original source of thesedata is the Harvard Seismological Group of Harvard University. Finally, we
obtained earthquake catalog containing the earthquake data from 1912 to2007 within 0oN to 30oN and 88oE to 110oE. Sample data from the final
updated earthquake catalogue are shown in Table 1.
Table 1. Sample data from the final updated earthquake catalogueoo

DEPTH km

mb

Ms

ML

Mw

94.15

33

5.0

4.5

-

-

94.11

25

5.1

4.5

-

-

5.00

94.00

-

5.3

-

-

-

18.0

6.06

94.29

33

5.6

4.5

-

-

20.2
20.0
58.5
4.0
10
8.3
31.1
52.2
51.9
42.4
54
6.9
30.5
30.6
25.8
26.0

22.15
22.30
14.21
22.16
22.20
22.71
24.29
23.76
23.72
23.22
23.90
8.18
11.07
11.04
10.92
11.00

94.22
94.12
96.75
98.92
98.70
96.22
93.14
94.31
94.20
94.61
94.20
92.72
92.7
92.97
93.22
92.80

167
167
10
14
33
33
33
85
83
33
55
65
68
33
-

4.0
4.0
4.2
4.5
4.9
4.8
5.2
4.7
4.1
4.9
4.7
5.1
-

5.4
4.1
4.7

3.9
4.6
5.1
-

-

No

Source

YR

MO

DA

HR

MN

SEC

LAT

1

USGS

1978

06

07

10

26

19.9

6.34

2

ISC

1978

06

07

10

26

19.0

6.29

3

HFS

1978

06

07

10

26

8.0

4

MOS

1978

06

07

10

26

5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

USGS
ISC
TMD
TMD
ISC
ISC
TMD
USGS
ISC
MOS
PEK
ISC
USGS
ISC
MOS
PEK

1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980
1980

05
05
05
05
05
05
05
05
05
05
05
05
05
05
05
05

06
06
07
14
14
16
20
20
20
20
20
25
26
26
26
26

22
22
15
08
08
19
13
13
13
13
13
22
11
11
11
11

23
23
38
47
47
00
19
19
19
19
19
57
37
37
37
37

LONG

2.2 Magnitude Conversion
Different magnitudes scales were reported for each earthquake event in the
final earthquake catalog depending on its original source. For example, thesurface wave magnitude (Ms) and the short period P-wave magnitude (mb)are commonly used in the data from USGS and ISC. While the local
magnitude (ML) is reported by TMD, and the moment magnitude (Mw) is
reported in the Harvard catalog.
For the catalog to be usable for developing seismic hazard map, it isnecessary to convert all earthquake events to a single magnitude scale. In
this project, we choose to convert all magnitude scales to be moment
magnitude (Mw) by a set of magnitude conversion equations as shown in
Table 2.

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Table 2. Magnitude conversion relations used in the study

In cases where earthquake event is reported in both mb and Ms, we choose
Ms over mb then convert Ms to Mw. The reason for choosing Ms over mb, mb
was calculated based on P-wave in the first five-second which it is observed
sometimes that the big earthquake wave which controls the earthquake
magnitude comes later.
In cases where the earthquake event is reported in both ML and mb, or ML
and MS, we choose mb & MS over ML. We then convert mb & MS to Mw.
This is because ML calculated from different region cannot be compared toeach other. In order to convert ML to the other magnitude scales, it isnecessary to provide the local magnitude conversion relation.
After the magnitude conversion, we remove the duplicate entries (fromdifferent data sources) to a single entry for each earthquake magnitude. The
catalog of unduplicated event shows 16,303 earthquake records withmagnitude greater than or equal to 5.
2.3 Declustering
In the earthquake source model, we assumed that the earthquake
sequence can be modeled as a Poisson distribution (time-independent).
Therefore, it is required that each earthquake entry in the catalog must be
free from each other. The earthquake catalog is then declustered to remove
dependent events such as foreshocks or aftershocks using the routines ofGardner and Knopoff (1974). Gardner and Knopoff identified duration, T,
and dimension, L, as functions of mainshock magnitude, M, for a set ofearthquake data, and fit least-upper-bound envelopes to the data of the form:log T or log L = aM + b. Following each earthquake in the chronologicallyordered catalog, we scan for events in a [T(M), L(M)] window. If there isan event with magnitude less than M, it is deleted as a foreshock oraftershock. If there is an event with magnitude more than M, the original
earthquake is deleted as a foreshock.
Declustering eliminates about 62 percent of the events in the catalogue. The
final declustered catalog shows 6136 earthquake events with Mw greaterthan or equal to 5 in the study region from 1912 to 2007. These earthquake
data are plotted in Figures 1-5.

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Figure 1: Earthquakes with magnitude lower than 3.0
in the study region from 1912 to 2007

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Figure 2: Earthquakes with magnitude lower than 4.0
in the study region from 1912 to 2007

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Figure 3: Earthquakes with magnitude lower than 5.0
in the study region from 1912 to 2007

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Figure 4: Earthquakes with magnitude lower than 6.0
in the study region from 1912 to 200799

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Figure 5: All earthquakes in the study region from 1912 to 2007

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2.4 Catalog CompletenessCompleteness of the earthquake catalog defines the times interval forearthquake events in a certain magnitude range that is completely recorded.
Completeness of earthquake catalog is different for each magnitude range
and each location depending on the quality of seismographic network in thatarea. The earthquake data in the catalog that fail to correct the data
incompleteness cannot be used in the earthquake source model because 1)
this data cannot be guaranteed to follow the Poison distribution assumption
and 2) this data may result in underestimation of the mean rate ofearthquake occurrence.
The completeness characteristic of the earthquake catalog in thisproject can be observed from Figure 6 which shows the plot between
moment magnitude (Mw) and year of record. It can be seen that, as time
passes, the smaller and smaller earthquakes can be detected. This is because
the seismographic network has been continually developed in Thailand. It
has been observed that the earthquake with magnitude less than 5.0 can bedetected in 1962 which is the year that the seismographic network was
drastically improved.

Figure 6: Distribution of earthquake data (Moment Magnitude, Mw)
according to the occurrence year in the study area
Two methods were employed for completeness analysis of thecatalog: (1) the Visual Cumulative method (CUVI) (Tinti and Mulargia,
1985), & (2) the Stepp method (Stepp, 1973). We divide the study region
into five zones – 3 subduction zones (SD-A, SD-B, SD-C), Thailand andsurrounding zone (BG-I), and the remaining zone (BG-II). These five zones
are shown in Figure 7.

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In CUVI method, the completeness times for different magnitude
ranges (says events greater than reference value, Mwref) can be determinedfrom the plot between the cumulative number of events and time for eventswith the magnitude between Mwref and Mwref+0.5. The example ofcompleteness analysis by CUVI method is shown in the plots for zone BG-I
(Figure 8 and 9). From each figure, the completeness time for eachmagnitude range is considered from the time period that the plot has aconstant slope (black line).

Figure 7: Background seismicity zones (BG-I and BG-II)and subduction zones (SD-A, SD-B, and SD-C)

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Mw < 3.0

1000
900

Cumulative number of events

800
700
600
500
400
300
200
100
0
1980

1985

1990

1995
Time(years)

2000

2005

2010

3.0 < Mw < 3.5

450

Cumulative number of events

400
350
300
250
200
150
100
50
0
1975

1980

1985

1990
1995
Time(years)

2000

2005

2010

2000

2005

2010

2000

2005

2010

3.5 < Mw < 4.0

350

Cumulative number of events

300
250
200
150
100
50
0
1975

1980

1985

1990
1995
Time(years)
4.0 < Mw < 4.5

250

Cumulative number of events

200

150

100

50

0
1975

1980

1985

1990
1995
Time(years)

Figure 8: Example of completeness analysis of the earthquake catalog
for Mw<4.5 by CUVI method in BG-I zone

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4.5 < Mw < 5.0

100
90

Cumulative number of events

80
70
60
50
40
30
20
10
0
1970

1975

1980

1985

1990
1995
Time(years)

2000

2005

2010

2005

2010

2000

2010

5.0 < Mw < 5.5

45

Cumulative number of events

40
35
30
25
20
15
10
5
0
1965

1970

1975

1980

1985 1990
Time(years)

1995

2000

5.5 < Mw < 6.0

16

Cumulative number of events

14
12
10
8
6
4
2
0
1930

1940

1950

1960

1970
1980
Time(years)

1990

Figure 9: Example of Completeness analysis of the earthquake catalog
for Mw>4.5 by CUVI method in BG-I zone

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The Stepp method uses the statistical procedures to assess thecompleteness times of the reported magnitudes, which assume theearthquake sequence in catalog can be modeled as a Poisson distribution. If
k1, k2, k3, … kn are the number of events per unit time interval, then :1 n ki
n i 1and its variance is n, where λ is the average of the occurrences in the
period T and n equals the number of unit time intervals. If unit time is one
year,  as the standard deviation of the estimate of the mean
where T is the sample length in years. The earthquake data in the catalog isused to calculate standard deviation ( for each sample years (T), andlog() and log(T) is plotted as shown in Figure 10. The completeness timeis determined from the portion of the line with T1/2. The completeness timefrom Stepp method for zone BG-1 and BG-2 are summarized in Table 3. It
can be observed that there is a good correlation between completeness time
obtained from CUVI and Stepp method. However, we chose the result fromCUVI method as we feel less subjective in determining completeness range.
The summary of the completeness period for the earthquake catalog isshown in Table 3 for BG-I and BG-II and Table 4 for subduction zones,
respectively.


1

10

Mw < 3.0
3.0 < Mw < 3.5
3.5 < Mw < 4.0
4.0 < Mw < 4.5
4.5 < Mw < 5.0
5.0 < Mw < 5.5
5.5 < Mw < 6.0
6.0 < Mw < 6.50log()

10

-1

10

-2

10

0

10

1

10

log(T)

2

10

3

10

Figure 10: Example of Completeness analysis of the earthquake catalog
by Stepp method in BG-I zon

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3. MODELLING OF EARTHQUAKE SOURCES
To properly describe the complex earthquake sources in the region, they aremodeled as a combination of background seismicity, subduction areasources, and crustal faults. They are described in more detail in thefollowing.
3.1 Background seismicity model
We use the background seismicity model to represent random earthquakes
in the whole study region except subduction zones. The model accounts forall earthquakes in areas with no mapped faults and for smaller earthquakes
in areas with mapped faults.
There are two different approaches to model background seismicity.
The first and traditional approach is to divide the region into many areas,
where a uniform rate of seismicity is assumed for each area. Earthquakes
are assumed to occur randomly at any location with an equal probability ineach area. Although this approach has been commonly and widely used in
the past, it has many drawbacks. Among these, the most serious drawback
is that the seismic hazard assessment results can be significantly affected by
the delineation of areas, which is heavily dependent on the subjective
judgment of the hazard analyst.
To resolve this drawback, the second approach—spatially smoothed
seismicity model—has been recently proposed for modeling backgroundseismicity. In this approach, it is not necessary to divide the region into
many areas. One large area may be used, but the rate of seismicity is
assumed to vary from place-to-place within the area. The rate of seismicity
is determined by first overlaying a grid with spacing, say, 0.1o in latitude
and 0.10 in longitude onto the region, and counting the number ofearthquakes with magnitude greater than a reference value (Mref) in eachgrid cell. The rate of seismicity is computed by dividing the number ofearthquakes by the time period of earthquake data. The rate is thensmoothed spatially by a Gaussian-function moving average. By thisapproach, the spatially varied seismicity can be modeled without being
affected by the subjectivity of the hazard analyst.
This spatially smoothed background seismicity model was used by
the US Geological Survey in the development of seismic hazard maps inCentral and Eastern United States (Frankel, 1995).
We adopt this spatially smoothed background seismicity model inthis study. The whole study region is divided into 5 source zones: BG-I,
BG-II, SD-A, SD-B, and SD-C (Figure 7). The zones SD-A, SD-B, andSD-C are subduction zones, which will be described in detail later. Thezone BG-I is a background seismicity zone covering Thailand and

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surrounding areas, and the zone BG-II is another background seismicity
zone covering the remaining areas except the 3 subduction zones.
Earthquake data, particularly small earthquakes, in BG-I are much
more completely recorded than those in BG-II (see Figure 1 to 5). This isdue to the high earthquake detection capability of a fairly dense
seismograph network in Thailand. Hence, the accuracy of estimatedseismicity rate in BG-I can be significantly improved by including data ofsmaller earthquakes (3.0 < MW < 5.0) in the seismicity rate calculation.
This is important because the seismicity rate inside Thailand is somewhere
between low to moderate. On the other hand, in BG-II only earthquake datawith MW > 5.0 can be used for computing the seismicity rate due to theincompleteness of small earthquake data. Nevertheless, lacking of small
earthquake data is not a major problem because the seismicity rate in thiszone is relatively high, so that the rate can be reliably estimated frommoderate earthquakes. In addition, the influence of BG-II on the seismic
hazard in Thailand is lower than that of BG-I.
We model the magnitude-dependent characteristic of seismicity ratein each background seismicity zone by a truncated exponential model(Gutenberg-Richter model):Log10 N(MW) = a – b MW

(1)

Where N(MW) is the annual occurrence rate of earthquakes withmagnitude greater than or equal to MW, and a and b are the model
parameters.
The b-value is assumed to be uniform throughout the wholebackground region. Hence, we used complete earthquake data withmagnitude greater than 4.0 in both BG-I and BG-II to compute a single
regional b value. The obtained regional b-value is 0.90, and this value isused for both BG-I and BG-II.
The a-value varies from place-to-place within each zone. It is
computed by using a grid with spacing of in latitude and longitude and isspatially smoothed using a two-dimensional Gaussian moving average
operator with a correlation distance parameter C. Earthquake data with MW> 3.0 and C = 50 km are used for BG-I, while earthquake data with MW >5.0 and C = 75 km are used for BG-II. The resulted smoothed rate 10avalues are presented in Figures 11 and 12.
In the truncated GR models of both BG-I and BG-II, the minimum
earthquake magnitude is set to 5.0 because earthquakes smaller than thislevel do not normally cause damage to buildings and structures. The
maximum (upper bound) magnitude is set to 7.5 for BG-II to account for
many large earthquakes in this zone as shown in Figure 7. On the other
hand, in BG-I large earthquakes are accounted for by crustal faults (details
are shown in crustal faults section), so the maximum magnitude is set to 6.5
to avoid the double-counting effect in this zone.

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Figure 11: Map of Smooth 10a values derived from Mw 3.0
and larger earthquakes in zone BG-I (correlation distance of 50 km)

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Figure 12:Map of Smooth 10a values derived from Mw 5.0
and larger earthquakes in zone BG-II (correlation distance of 75 km)

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3.2 Subduction zone modelAs explained earlier, the megathrust Sunda subduction zone is divided into
3 sub zones based on seismicity characteristics: Burma (SD-A), Northern
Sumatra-Andaman (SD-B), and Southern Sumatra (SD-C). Each sub zone
is modeled as a seismic area source with a uniform rate of seismicity (thetraditional area source model), and the magnitude-dependent characteristicof seismicity rate is modeled by a truncated GR model.
The calculations to identify GR model parameters (a and b values)
are shown by Figures 13-15. The minimum earthquake magnitude in the
GR model is set to 6.5 as the subduction zones are very far from Thailand.
The maximum magnitude for zone SD-A is set to 8.1, which is equal to the
maximum observed magnitude plus 0.5. The maximum magnitude for zone
SD-B and SD-C is set to 9.2 as the 2004 Sumatra earthquake and the 2005
Nias earthquake has already demonstrated the capability of the zones togenerate large mega thrust earthquakes.

Figure 13: Calculation sheet to determine the GR model parametersof subduction zone SD-A

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Figure 14: Calculation sheet to determine the GR model parametersof subduction zone SD-B

Figure 15: Calculation sheet to determine the GR model parametersof subduction zone SD-C

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A subduction earthquake in each zone is assumed to be created by arupture along an inclined plane at the interface between two tectonic plates.
The plane is 5 km deep at the left-hand-side zone boundary and is 50 km
deep at the right-hand-side zone boundary. This assumption is based on a
recent work on ‘Teleseismic relocation and assessment of seismicity (19182005) in the region of the 2004 MW 9.0 Sumatra-Andaman and 2005 MW
8.6 Nias Island great earthquakes’ by E. Robert Engdahl and his coworkers, presented at the 6th General Assembly of the Asian Seismological
Commission in Bangkok in 2006 (ASC2006).
3.3 crustal fault source model
Twenty-one crustal fault sources are modeled explicitly in this study as
shown in Figure 16. Their important properties and parameters areillustrated by logic tree diagrams in Figures 17-37.

Figure 16: Map of crustal fault sources inand near Thailand considered in this study

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Figure 17: Logic tree diagram of Tavoy fault

Figure 18: Logic tree diagram of Kungyaungale fault

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Figure 19: Logic tree diagram of Chao Phraya Basin fault

Figure 20: Logic tree diagram of Three Pagoda Fault Zone

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Figure 21: Logic tree diagram of Ranong fault

Figure 22: Logic tree diagram of Khlong Marui fault

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Figure 23: Logic tree diagram of Chumporn Basin fault

Figure 24: Logic tree diagram of Tenasserim fault

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Figure 25: Logic tree diagram of Phrae Basin fault

Figure 26: Logic tree diagram of Phrae fault

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Figure 27: Logic tree diagram of Phayao fault

Figure 28: Logic tree diagram of Thoen fault

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Figure 29: Logic tree diagram of Long fault

Figure 30: Logic tree diagram of Pua fault

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Figure 31: Logic tree diagram of Nam Pat fault

Figure 32: Logic tree diagram of Sri Sawat fault

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Figure 33: Logic tree diagram of Bong Ti fault

Figure 34: Logic tree diagram of Mae Chan fault

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Figure 35: Logic tree diagram of Mae Kuang fault

Figure 36: Logic tree diagram of Moei fault

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Figure 37:Logic tree diagram of Sagaing fault
The information about crustal faults in and near Thailand are mainly
obtained from recent paleoseismic investigations carried out by WoodwardClyde Federal Services in Northern (DMR, 1996), Western (EGAT, 1998),
and Southern Thailand (RID, 2005). The investigations were carried out
using an appropriate mix of remote sensing imagery, aerial photographic
interpretation, and field investigation. The investigation concentrated on thegeomorphic expression of faulting, and the comparison of these featureswith the features observed along other active faults around the world.
The Sagaing fault in Myanmar is a major right-lateral strike-slip
fault in the study region. The high assigned slip rate of 18 mm/yr is basedon high rates of strain accumulation suggested by GPS studies (Socquet etal. 2006 and USGS 2007).
The occurrence rate of large magnitude earthquakes on crustal faults
is determined from long-term slip rates and characteristic earthquake
magnitude (MC). MC is estimated from expected rupture length, which may
be limited by fault segmentation, by using the relation proposed by Wells
and Coppersmith (1994).
Two different approaches are employed to model the magnitudedependent characteristic of seismicity rate of these crustal faults: GR modeland Characteristic Earthquake (CE) model.
In GR model, a GR magnitude-frequency distribution is assumed
from the minimum magnitude of 6.5 to the upperbound magnitude (Mmax).
To account for the uncertainty in estimating Mmax, we consider threedifferent cases with Mmax set equal to MC-0.2, MC, and MC+0.2. The
probabilistic weights of 0.2, 0.6, and 0.2 are assigned to these cases,
respectively. In each case, the b-value is set equal to the regional b-value of0.90, and the a-value is determined from the seismic moment rate, which iscomputed from the fault slip rate.

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In CE model, three characteristic earthquake magnitudes are also
considered: MC-0.2, MC, and MC+0.2. The probabilistic weights of 0.2, 0.6,
and 0.2 are assigned to these cases, respectively. In each case, theearthquake occurrence rate is computed from the characteristic magnitude
and the fault slip rate (to match with the seismic moment rate of the fault),
and the magnitude is assumed to be normally distributed around thecharacteristic value with a standard deviation of 0.12.
4. ATTENUATION MODELSThe ground motion parameters such as PGA and SA at a given site can be
estimated from the earthquake magnitude, source-to-site distance, and local
site condition by using attenuation models. Traditionally an attenuation
model for a specific region is empirically developed from statisticalregression analyses of hundreds of earthquake ground motion records. For
this region, however, a very limited number of strong motion records areavailable. One solution to this data limitation is to assume that someexisting attenuation models developed for other regions with similar
seismotectonic characteristics can adequately represent ground motion
attenuation in this region.
In this study, three NGA (Next Generation Attenuation of Ground
Motions) attenuation models developed for shallow crustal earthquakes inWestern United States and similar active tectonic regions are applied forbackground earthquakes in BG-I and BG-II and for earthquakes from crustal
faults in the study region. These NGA models are developed by Boore andAtkinson (2007), Campbell and Bozorgnia (2007), and Chiou and Youngs(2007) under the Next Generation Attenuation Relation project. We assign
equal probablilistic weight to each of these three models in the logic trees
analysis.
For subduction zone earthquakes in SD-A, SD-B and SD-C, we
adopt three subduction zone attenuation models developed by Youngs et al.
(1997), Atkinson and Boore (2003, global model), and Zhao et al. (1997).
Probabilistic weights assigned to these models are 0.25, 0.25, and 0.50,
respectively.
The above selected sets of attenuation models as well as their corresponding
probabilistic weights are identical to those used by USGS in their recent
seismic hazard study of Southeast Asia in 2007 (USGS, 2007). These
attenuation models are illustrated by Figures 38-60.

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November,
November, 20092009
PGA - 
Campbell & 
Bozorgnia et 
al 
2008


0


10


-1

Acceleration(g)


10


-2


10


M 
6.5
 
M
7.0

M 
7.5


-3


10


0


1


10


10


2


Distance(km)


10


3


10

Figure 38: Campbell & Bozorgnia (2008) NGA attenuation relation
for peak ground acceleration
SA(0.2sec) - 
Campbell & 
Bozorgnia et 
al 
2008


1


10


0

Spectral Acceleration(g)


10


-1


10


-2


10


M 
6.5
 
M
7.0

M 
7.5


-3


10


0


10


1


10


2


Distance(km)


10


3


10

Figure 39:Campbell & Bozorgnia (2008) NGA attenuation relation
for spectral acceleration at 0.2 sec

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SA(1.0sec) - 
Campbell & 
Bozorgnia et 
al 
2008

0

Spectral Acceleration(g)

10


-1


10


-2


10


M 
6.5

M 
7.0

M 
7.5


-3


10


0


1


10


10


2


Distance(km)

10


3


10


Figure 40: Campbell & Bozorgnia (2008) NGA attenuation relation
for spectral acceleration at 1.0 sec

SA(2.0sec) - 
Campbell & 
Bozorgnia et 
al 
2008

0

Spectral Acceleration(g)

10


-1


10


-2


10


M 
6.5

M 
7.0

M 
7.5


-3


10


0


10


1


10


2


Distance(km)

10


3


10


Figure 41: Campbell & Bozorgnia (2008) NGA attenuation relation
for spectral acceleration at 2.0 sec

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November,
November, 20092009
PGA - 
Boore & Atkinson et 
al 
2008


3


10


2


10

Acceleration (cm/sec2)


1


10


0


10


-1


10


-2


10


M 
6.5
 
M
7.0

M 
7.5


-3


10


-4


10


0


1


10


10


2

 Distance
(km)


10


3


10

Figure 42: Boore & Atkinson (2008) NGA attenuation relation
for peak ground acceleration
SA(0.2sec) - 
Boore & Atkinson et 
al 
2008


3


10


2

Spectral Acceleration(cm/sec2)


10


1


10


0


10


-1


10


-2


10


M 
6.5
 
M
7.0

M 
7.5


-3


10


0


10


1


10


2


Distance 
(km)


10


3


10

Figure 43:Boore & Atkinson (2008) NGA attenuation relation
for spectral acceleration at 0.2 sec

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 &
 Atkinson et al 2008SA(1.0sec) - Boore
3Spectral acceleration (cm/sec2)


10


2


10


1


10


0


10


M 
6.5

M 
7.0

M 
7.5


-1


10


0


1


10


10


2


Distance(km)


10


3


10

Figure 44: Boore & Atkinson (2008) NGA attenuation relation
for spectral acceleration at 1.0 sec


 &
 Atkinson et al 2008SA(2.0sec) - Boore
3Spectral Acceleration (cm/sec2)


10


2


10


1


10


0


10


M 
6.5

M 
7.0

M 
7.5


-1


10


0


10


1


10


2


Distance 
(km)


10


3


10

Figure 45: Boore & Atkinson (2008) NGA attenuation relation
for spectral acceleration at 2.0 sec

39
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November,
November, 20092009
SA(0.2sec) - 
Chiou & 
Young et 
al 
2008


1


10


0


10

Spectral Acceleration (g)


-1


10


-2


10


-3


10


-4


10


M 
6.5
 
M
7.0

M 
7.5


-5


10


-6


10


0


1


10


10


2


Distance 
(km)


10


3


10

Figure 46: Chiou & Youngs (2008) NGA attenuation relation
for spectral acceleration at 0.2 sec
SA(1.0sec) - 
Chiou & 
Young et 
al 
2008


0


10


-1

Spectral Acceleration(g)


10


-2


10


-3


10


M 
6.5
 
M
7.0

M 
7.5


-4


10


0


10


1


10


2


Distance 
(km)


10


3


10

Figure 47: Chiou & Youngs (2008) NGA attenuation relation
for spectral acceleration at 1.0 sec

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SA(2.0sec) 
Chiou & 
Young et 
al 
2008

0


10


-1


Spectral Acceleration (g)

10


-2


10


-3


10


M 
6.5

M 
7.0

M 
7.5


-4


10


0


1


10


10


2


Distance(km) 

10


3


10


Figure 48: Chiou & Youngs (2008) NGA attenuation relation
for spectral acceleration at 2.0 sec

PGA - Zhao et 
al 
2006


0


10


-1


Acceleration (g)

10


-2


10


-3


10


-4


10


M 
7.0

M 
8.5

M 
9.2


-5


10


0


10


1


10


2


Distance (km)


10


3


10


Figure 49: Zhao (2006) Subduction attenuation relation
for peak ground acceleration

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November,
November, 20092009
SA(0.2sec) - Zhao et 
al 
2006


1


10


0

Spectral Acceleration (g)


10


-1


10


-2


10


-3


10


-4


10


M 
7.0
 
M
8.5

M 
9.2


-5


10


0


1


10


10


2


Distance 
(km)


3


10


10

Figure 50: Zhao (2006) Subduction attenuation relation
for spectral acceleration at 0.2 sec
 et al 2006SA(1.0sec)- Zhao


0


10


-1

Spectral Acceleration (g)


10


-2


10


-3


10


M 
7.0
 
M
8.5

M 
9.2


-4


10


0


10


1


10


2


Distance 
(km)


3


10


10

Figure 51: Zhao (2006) Subduction attenuation relation
for spectral acceleration at 1.0 sec

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SA(2.0sec) - Zhao et 
al 
2006


0


10


-1

Spectral Acceleration (g)


10


-2


10


-3


10


M 
7.0

M 
8.0

M 
9.2


-4


10


0


1


10


10


2

 Distance
(km)


10


3


10

Figure 52: Zhao (2006) Subduction attenuation relation
for spectral acceleration at 2.0 sec


PGA - 
Young et 
al 
1997


0


10


-1

Acceleration (g)


10


-2


10


-3


10


M 
7.0

M 
8.5

M 
9.2


-4


10


0


10


1


10


2


Distance 
(km)


10


3


10

Figure 53: Young (1997) Subduction attenuation relation
for peak ground acceleration

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November,
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
SA(0.2sec) - 
Young et 
al 19970Spectral Acceleration (g)


10


-1


10


-2


10


M 
7.0
 
M
8.5

M 
9.2


-3


10


0


1


10


10


2

 Distance
(km)


3


10


10

Figure 54: Young (1997) Subduction attenuation relation
for spectral acceleration at 0.2 sec
SA(1.0sec) - 
Young et 
al 
1997


0


10


-1

Spectral Acceleration (g)


10


-2


10


-3


10


M 
7.0
 
M
8.5

M 
9.2


-4


10


0


10


1


10


2


Distance 
(km)


3


10


10

Figure 55: Young (1997) Subduction attenuation relation
for spectral acceleration at 1.0 sec

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SA(2.0sec) - 
Young et 
al 
1997

0


10


-1


Spectral Acceleration (g)

10


-2


10


-3


10


M 
7.0

M 
8.5

M 
9.2


-4


10


0


1


10


10


2


Distance (km)


10


3


10


Figure 56: Young (1997) Subduction attenuation relation
for spectral acceleration at 2.0 sec

PGA - Atkinson & 
Boore et 
al 
2003


3


10


2


Acceleration (cm/sec2)

10


1


10


0


10


-1


10


M 
7.0

M 
8.5


-2


10


1


10


2


10

Distance(km) 

3


10


Figure 57: Atkinson & Boore (2003) Subduction attenuation relation
for peak ground acceleration

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November,
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 & Boore et al 2003SA(0.2sec) - Atkinson


3


10


2

Spectral Acceleration (cm/sec2)


10


1


10


0


10


-1


10


M 
7.0

M 
8.5


-2


10


1


2


10


10

Distance 
(km)


3


10

Figure 58: Atkinson & Boore (2003) Subduction attenuation relation
for spectral acceleration at 0.2 sec
 & Boore et al 2003SA(1.0sec) - Atkinson

3

Spectral Acceleration (cm/sec2)


10


2


10


1


10


0


10


M 
7.0

M 
8.5


-1


10


1


10


2


10

Distance 
(km)


3


10

Figure 59: Atkinson & Boore (2003) Subduction attenuation relation
for spectral acceleration at 1.0 sec

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2Spectral Acceleration (cm/sec2)

10
 & Boore et al 2003SA(2.0sec) - Atkinson


1


10


0


10

1

10


M 
7.0

M 
8.5

2


10
 Distance
(km)


3


10

Figure 60: Atkinson & Boore (2003) Subduction attenuation relation
for spectral acceleration at 2.0 sec

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5. PROBABILISTIC SEISMIC HAZARD ANALYSISThe probabilistic seismic hazard analysis (PSHA) is carried using the USGS
software for PSHA (Harmsen, 2007). This is an open-source software in
Fortran-95 code developed and used by the USGS for making and updating
the US National Seismic Hazard map.
5.1 Results
5.1.1 Seismic Hazard Maps
The PSHA results are presented by several seismic hazard maps:
 map of peak ground acceleration with a 10-percent probability ofexceedance in a 50-year exposure period (Figure 17) map of peak ground acceleration with a 2-percent probability ofexceedance in a 50-year exposure period (Figure 18)
 map of spectral acceleration at 0.2 sec with a 2-percent probabilityof exceedance in a 50-year exposure period (Figure 19) map of spectral acceleration at 1.0 sec with a 2-percent probabilityof exceedance in a 50-year exposure period (Figure 20) map of spectral acceleration at 2.0 sec with a 2-percent probabilityof exceedance in a 50-year exposure period (Figure 21)
All these seismic hazard maps are made using a reference site
condition that is specified to be the boundary between NEHRP (National
Earthquake Hazards Reduction Program) classes B and C (rock site), withan average shear-wave velocity in the upper 30 m of the crust of 760 m/s.
The PGA map for 10 percent probability of exceedance (10% Pe),
which corresponds to a return period of 475 years, shows a pattern ofseismic hazard that is well correlated with the seismicity rate 10a (see Figure8). The effect of low-slip-rate crustal faults in Thailand on seismic hazard is
not so clear on this map. On the other hand, the effect of crustal faults
becomes evidence in the PGA map (as well as spectral acceleration maps)
for 2% Pe, which corresponds to a return period of 2475 years. The map
clearly shows locally high seismic hazard levels at the locations of somecrustal faults. The results confirm the important of crustal fault sources on
the seismic hazard of Thailand.
The computed probabilistic ground motion parameters at someselected locations in Thailand are compared with those obtained from otherearlier seismic hazard studies in Table 5.

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Figure 61: Map of peak ground acceleration with a 10-percent probabilityof exceedance in a 50-year exposure period (unit: g)49
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Figure 62: Map of peak ground acceleration with a 2-percent probabilityof exceedance in a 50-year exposure period (unit: g)

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Figure 63: Map of spectral acceleration at 0.2 sec with a 2-percent
probability of exceedance in a 50-year exposure period (unit: g)51
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Figure 64: Map of spectral acceleration at 1.0 sec with a 2-percent
probability of exceedance in a 50-year exposure period (unit: g)

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Figure 65: Map of spectral acceleration at 2.0 sec with a 2-percent
probability of exceedance in a 50-year exposure period (unit: g)53
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Table 5: Comparison of probabilistic ground motion parameters from thecurrent study with those obtained from other seismic hazard studies

5.1.2 Seismic Hazard in Bangkok
The seismic hazard in Bangkok is contributed by earthquakes frommany sources near and far. To gain insight into this issue, hazard curves
obtained from the analysis of spectral acceleration at four periods ofvibration are shown in Figures 62-65. The contributions from different
earthquake sources are illustrated by disaggregated hazard curves, where
each curve represents the hazard from only one earthquake source. The
results show that the seismic hazard at short period (0.2 sec) is contributed
mainly by crustal faults and to a lesser extent by background seismicity.
The contribution from distant subduction earthquakes is negligibly low.
This contribution, however, rapidly increases with the increasing in period,and finally becomes the dominant one for hazard at long periods (2.0 and
3.0 sec).
A uniform hazard spectrum for Bangkok is then constructed fromthe computed PGA and spectral accelerations for 2% Pe as shown by theviolet line in Figure 66. The green, red, and blue lines represent the hazard
contributions from subduction zones, crustal faults, and backgroundseismicity, respectively. Although the analysis is made up to the period of
3.0 sec, the hazard spectrum is extended up to the period of 6.0 sec byassuming that the spectral acceleration is inversely proportional to period.

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RNUS Annual Report 2008 and 2009contributions from subduction zones, crustal faults, and backgroundseismicity, respectively. Although the analysis is made up to the period of
3.0 sec, the hazard spectrum is extended up to the period ICUS
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assuming that the spectral acceleration is inversely proportional to period.
Since the uniform hazard spectrum of violet line is made for the
NEHRP site class B/C (rock site), it is then transformed into the UHS(Uniform Hazard spectrum) for soft Bangkok site condition.
The
transformation is made by multiplying the UHS for site class B/C with theperiod-dependent spectral amplification ratio in the column “average”
average inTable 5. The period-dependent spectral amplification ratio is estimatedfrom the transfer functions of Bangkok soil profiles A2 and B1 (see pages
42 and 43 in CPI, 2009) and assumed frequency composition of groundshaking corresponding to the spectral period. The spectral amplification
ratios at 0.2 sec and 1.0 sec are similar to those of NEHRP site class E (softsoil site) and those at long periods (2.0-6.0 sec) are similar to those of
ARUPs study.
ARUP’s
The resulting UHS for soft Bangkok soil is shown by the orange line67.
in Figure 66 and the green line in Figure 67.

-1Annual Frequency of Excedance10

Bangkok (13.7
N 
100.4
E)
Background 
Seismicity
Crustal 
Faults
Subduction Zone
All 
Sources


-2


10


-3


10


-4


10 
-3

10


-2


10


-1


SA 
0.2 
sec(g)


10


0


10

Figure 66: Computed hazard for spectral acceleration at 0.2 sec in Bangkok,
and contributions of three different earthquake sources to the hazard

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
-1

Annual Frequency of Excedance10
Bangkok 
(13.7
N 
100.4
E)
Background 
Seismicity

Crustal Faults
Subduction Zone
All 
Sources


-2


10


-3


10


-4


10

-3

10


-2


10


-1


 
SA 1.0sec(g)


0


10


10

Figure 67: Computed hazard for spectral acceleration at 1.0 sec in Bangkok,
and contributions of three different earthquake sources to the hazard

-1Annual Frequency of Excedance10
Bangkok 
(13.7
N 
100.4
E)
Background 
Seismicity

Crustal Faults
Subduction Zone
All 
Sources


-2


10


-3


10


-4


10

-3

10


-2


10


-1


 
SA 2.0sec(g)


10


0


10

Figure 68: Computed hazard for spectral acceleration at 2.0 sec in Bangkok,
and contributions of three different earthquake sources to the hazard

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40-1Annual Frequency of Excedance10
Bangkok 
(13.7
N 
100.4
E)
Background 
Seismicity
Crustal Faults

Subduction Zone

All 
Sources


-2


10


-3


10


-4


10

-3

10


-2


10


-1


SA 3.0sec(g) 

10


0


10


Figure 69: Computed hazard for spectral acceleration at 3.0 sec in Bangkok,
and contributions of three different earthquake sources to the hazard

Figure 70: Uniform Hazard Spectrum of Bangkok for 2-percent probabilityof exceedance, and contributions of three earthquake sources to the pectrum

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Figure 71: Comparison of uniform hazard spectra
REFERENCES
Atkinson, G.M., and Boore, D.M., (2003), “Empirical ground-motion
relations for Subduction-zone earthquakes and their application to
Cascadia and other regions”, Bull. Seism. Soc. Am., Vol. 93, pp 17031729.
Boore, D.M. and Atkinson, G.M. (2008), “Ground-motion prediction
equations for the average horizontal component of PGA, PGV, and 5%damped PSA at spectral periods between 0.01 s and 10.0 s”,
Earthquake Spectra, Vol. 24, No. 1, pp 99-138.
Campbell, K.W., and Bozorgnia, Y. (2008), “Ground motion model for thegeometric mean horizontal component of PGA, PGV, PGD and 5%
damped linear elastic response spectra for periods ranging from 0.01 to
10.0 s”, Earthquake Spectra, Vol. 24, No. 1, pp 139-171.
Chiou, B., and Youngs, R. (2008), “A NGA model for the average
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DMR (1996) Department of Mineral Resources, Environmental Impact
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Heaton, T.H., Tajima, F. and Mori, A.W. (1986), “Estimating groundmotions using recorded accelerograms”, Surveys in Geophysics, Vol. 8,
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Wells, D.L. and Coppersmith, K.J. (1994), “New empirical relationships
among magnitude, rupture length, rupture width, and surface
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Zhao, J. X., Zhang, J., Asano, A., Ohno, Y., Oouchi, T., Takahashi, T.,
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Seism. Soc. Am., Vol. 96, pp 898-913.

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List of Figures
ListListof Figuresof Figuresof FiguresofListFiguresFigure 1: Earthquakes
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38
38

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2008 2008

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November,
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November,November,November,
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2009

FigureFigureFigure44:44:
44:
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