s

1. J. Resour. Ecol. 2011 2(1) 83-90
DOI:10.3969/j.issn.1674-764x.2011.01.012
www.jorae.cn
March, 2011 Journal of Resources and Ecology
Received: 2010-12-07 Accepted: 2011-03-12
Foundation: National Science and Technology Support Program (Grant NO.2008BAK50B05, 2008BAK50B06).
* Corresponding author: WU Shaohong. Email:wush@igsnrr.ac.cn.
Vol.2 No.1
Article
1 Introduction
Earthquakes are sudden natural disasters which can cause
serious damage. The damage they cause generally includes
building collapse, human casualties and economic losses
(Gao et al. 2007). The loss of human life is the most
significant consequence of earthquakes. It is important to
assess seismic mortality risks, because the primary task of
earthquake reduction is to reduce human casualties.
It is necessary to predict and estimate earthquake-
induced casualties scientifically and then we take
appropriate measures that correspond with the possible
scale of earthquake casualties. Seismic mortality risk
assessment and management has become an important
method for effective disaster prevention and reduction,
and it has been advocated and promoted widely (Zhang
et al. 2006). On the background of disaster prevention
and reduction, it is necessary to accurately assess seismic
mortality risks when the work transferring from disaster
emergency management to pre-disaster risk prevention.
Seismic mortality risk assessment can provide a scientific
basis for disaster prevention and mitigation planning before
earthquake occurs, and it can help develop emergency
management programs for use during an earthquake and
for post-earthquake rescue and relief. It is one of the most
important ways to reduce earthquake casualties.
Research into seismic risk assessment began relatively
late in China. Considerable research effort has focused
on qualitative assessment (Ge et al. 2008), but there has
been no quantitative computation of seismic mortality
risk. Too few studies have been conducted at quantitative
or semi-quantitative levels. There are many limitations in
the expression of risk results, which are mostly relative
levels of risk loss (Anbalagan et al. 1996; Shi et al. 2006;
Zhu et al. 2002; Remondo et al. 2005; Lee et al. 2007;
Guzzetti et al. 1999; Sarris et al. 2009). These analyses of
earthquake risk without concrete risk loss values do not
reflect mortality differences between earthquakes levels,
Quantitative Assessment of Seismic Mortality Risks in China
XU Zhongchun1,2
, WU Shaohong1
*, DAI Erfu1
and LI Kaizhong1,2
1 Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
2 Graduate University of Chinese Academy of Sciences, Beijing 100049, China
Abstract: Based on the forming mechanism of seismic hazard risk, we established a seismic vulnerability
curve on population and determined earthquake occurrence parameters. We then assessed the risk of
seismic hazard mortality at the county level across China using the assessment model, and analyzed spatial
patterns. We adopted past, present, and future disaster-breeding materials to assess the probability of
earthquakes. In order to determine the earthquake parameters of 2355 counties accurately, we integrated
history seismic intensities, seismic activity fault belts distributions and seismic peak ground acceleration.
Based on data of seismic disasters from 1990 to 2009 in China, linear fitting between seismic intensities
and mortalities was performed. And a vulnerability curve of seismic mortality, which was appropriate
for seismic risk assessment, was established. Seismic mortality risks were assessed quantitatively at
the county level using the model and the spatial patterns were analyzed. Seismic mortality risks of 2355
counties with intensities from Ⅴ to Ⅺ were analyzed thoroughly. This study indicates that under different
seismic intensities, China’s eastern and central regions are generally confronted with higher risk than
western regions. High-risk areas are scattered in Shandong and Jiangsu, northern Anhui and eastern
Heilongjiang and Jilin, where populations are dense and the environment is conducive to disasters. Risk-
free areas displayed patchy distributions nationwide, and patterns were mostly unchanged.
Key words: seismic hazards; risk assessment; seismic mortality risks; China

2. Journal of Resources and Ecology Vol.2 No.1, 2011
84
and therefore they do not meet the needs of earthquake
prevention and reduction. Under the directions of
quantitative assessment, regional integration and spatial
management, the primary goal of disaster prevention and
reduction work is to determine quantitative risk values and
concrete loss from different earthquakes grades.
China is located between the Eurasian and Pacific
earthquake belts, and this specific geographical position
is responsible for the well-developed fault belts. So
China suffers from frequent earthquakes, characterized
by great intensities, wide distributions, shallow seismic
sources and huge human losses (Wang et al. 2006).
According to statistical data recorded by instruments
since the 20th century, Chinese earthquakes occupy
33% of global continental earthquakes. There have been
thirty earthquakes with a magnitude greater than 5, six
earthquakes with a magnitude greater than 6 and one
earthquake with a magnitude greater than 7 every year
(Pan et al. 2002). In general, there are 210 cities located at
intensities >6, accounting for 70% of Chinese total cities
numbers. But cities with intensities ≥7 occupy only 60%
of total cities numbers (Chinese Academy of Building
Research 2008). Chinese population base is large, so
exposures to earthquakes are vast, and since China is a
developing country, the ability to prevent and reduce the
damage from earthquake is still limited.
The factors mentioned above highlight that earthquake
damages in China are serious and seismic mortality risks
are generally high. So our knowledge of risk assessment
and emergency prevention is essential for reducing this
damage. However, earthquake prevention and reduction
work is confronted with many limitations, including
unknown distributions of seismic mortality risks,
inadequate estimation of disaster situations, improper
preparation for earthquakes and lack of pertinence and
scientific methods. In order to increase our understanding
of seismic mortality risk distributions and promote
earthquake prevention and disaster reduction effectively,
China urgently needs to quantitatively assess seismic risk.
Here we quantitatively assess seismic mortality risks at
the county level using the model and then analyze their
spatial patterns. This work will provide a scientific basis
for Chinese earthquake prevention and reduction.
2 Methodology
2.1 Data sources
2.1.1 Zoning map of county administrative unit
The zoning map of county administrative unit (2004)
was provided by the National Geomatics Center of China
(NGCC), State Bureau of Surveying and Mapping of
China.
2.1.2 Population data
The population data of all provinces at the county level
(2008) was provided by the research group. We adjusted
the Administrative Map from 2004 to vector map with the
actual situation.
2.1.3 Seismic loss data
Considering that populations are continually increasing,
an earthquake of the same magnitude in the past may
cause many more casualties today. Therefore, when
selecting past earthquake cases, those that occurred more
recently were preferred. Representatives, timeliness,
and the availability of past earthquake cases needed to
be considered comprehensively. There are 257 Chinese
destructive earthquake catalogs that have been collected
and processed mainly from 1990 to 2009 (China
Earthquake Data Center [http://www.china-disaster.cn];
Monitoring and Prediction Division of China Earthquake
Administration; Disaster Reduction Center of Chinese
Academy of Sciences (collected and processed earthquake
data (2000–2008) ,which was published by the National
Disaster Reduction Center, Ministry of Civil Affairs of
China; National Bureau of Statistics et al. 1995). Using
the catalogs, an earthquake database was established for
the earthquake loss criteria, which would be useful for
calculating a model (Steinbrugge et al. 1969; Chen et al.
1999; Wang et al. 2005).
According to the earthquake data from 1990–2009,
there were 12 earthquakes that caused exposures loss each
year. There were 3518 fatalities, and 22134 injuries on
average and the direct economic loss was 44.381 billion
CNY.
2.1.4 Disaster-breeding environment data
The patterns of disaster-breeding environments control
earthquake occurrence and distribution. Due to differences
in disaster-breeding environments, the probability of
earthquakes in different areas is not equal. It is generally
believed that areas where earthquakes have occurred or
where geological activity fault belts are fully developed
will have high probability of earthquakes (Xu et al. 2004;
Fang et al. 2002; Xu et al. 2003; Deng et al. 1978, 2002;
Chen 1984). Based on the above theoretical analysis, the
disaster-breeding environment data consists of seismic
faults, past seismic characteristics and seismic fortification
level.
(1) Integrated intensities of history earthquakes
In recent years, there were only two destructive
earthquakes in Wenchuan and Yushu that had large
magnitudes. According to these two earthquakes, the
Compilation Committee of National Seismic Zoning
developed the first and the second seismic ground motion
parameter zoning map of China. Based upon the digital
integrated contour map of history seismic intensities
(Comprehensive disaster study group of Ministry of
Science and Technology, State Planning Commission
and State Economic and Trade Commission of China,
2004), we updated the contour map by integrating the

3. XU Zhongchun, et al.: Quantitative Assessment of Seismic Mortality Risks in China 85
8.0 earthquake intensity distribution map in Wenchuan
(Experts group of earthquake resistance and disaster relief,
national disaster-reduction committee and Ministry of
Science and Technology 2008) and the assessment map of
Yushu earthquake (Restoration and reconstruction group of
Yushu earthquake 2010), to reflect the latest achievements
of Chinese history seismic intensities (Fig. 1).
(2) Seismic activity fault belts
The seismic activity fault belt distribution map of China
(Liao et al. 2000) was spatially overlaid with the Chinese
county-level administrative division map. The distributions
of seismic activity fault belts in every county were then
determined by manual interpretation (Fig. 2).
(3) Seismic peak ground acceleration
Using the seismic peak ground acceleration zonation
map of China (State Bureau of Quality and Technical
Supervision 2001; Hu et al. 2005), we updated seismic
peak ground acceleration by adding zonation maps of
Sichuan, Gansu and Shaanxi; Qinghai and Sichuan
provinces (Compilation Committee of National Seismic
Zoning Map 2008, 2010) (Fig. 3).
2.2 Model method
Risk was defined as expected loss due to a particular
hazard for a given area and reference period, given by the
combination of disaster intensities and disaster-bearing
bodies. Seismic risk was defined as the probability of
loss directly provoked by earthquakes, loss that may be
suffered by the population, the building and the economic
system. It resulted from joint effects between seismic
intensities and different disaster-bearing bodies.
Based upon the mechanisms to form seismic risk and by
Fig. 1 Integrated map of Chinese history seismic intensities.
Legend
Seismic intensity
County boundary
China land boundary
Provincial capital cites
≦Ⅴ
Ⅵ
Ⅶ
Ⅷ
Ⅸ
Ⅹ
≧Ⅺ
Fig. 2 Seismic activity fault belts distribution map of China.
Legend
Seismic fault belts
County boundary
China land boundary
Provincial capital cites

4. Journal of Resources and Ecology Vol.2 No.1, 2011
86
referring to previous models (United Nations Disaster
Relief Organization 1991; Chen et al. 1997, 1999; Wang et
al. 2006; Chan et al. 1998; Chen et al. 1998, 2001; Nadim
et al. 2009; Zonno 2003), we built the following model
and then applied it to assess seismic mortality risks.
R = (D×E )×P
R is seismic mortality risk; D is damaging criteria. It is
population susceptibility of different seismic intensities. E
is population exposure of different counties. And P is the
probability of earthquake occurrence.
When applying the model, it was difficult to establish
seismic damaging criteria and determine earthquake
occurrence probability, which are keys to seismic risk
assessment.
2.2.1 Seismic damaging criteria
There are three indexes that can characterize earthquake
activity: seismic magnitude, seismic intensity and peak
ground acceleration. Earthquake intensity indexes would
be screened by processing and analyzing Chinese seismic
loss data for 20 years. Compared with seismic magnitude,
there is a stronger correlation between seismic intensity
and seismic loss (Table 1). We chose to use seismic
intensity to characterize earthquake activity.
The best way to establish seismic damaging criteria is
to perform physical experiment simulations; but current
conditions did not permit these experiments because the
earthquake disaster system was extremely complex. By
referring to Tanshan seismic mortality (Chen et al. 1994),
we established seismic damaging criteria using the data of
seismic disasters from 1990 to 2009 in China (Table 2).
Exponential curve fitting between seismic intensities and
mortalities was conducted (Carrar et al. 2008; Chen et al.
1997), and the vulnerability curve of seismic mortality was
established. Its correlation coefficient was 0.8845, which is
a good fit.
-5 0.8185x
y 3 10 e
= ×
y is seismic mortality; x is seismic intensity.
2.2.2 Earthquake occurrence probability
The occurrence of earthquakes is closely related to
geological fault movement, and there exists great
differences in seismic activity at different places. In
order to determinate earthquake occurrence probability,
the structure of Chinese geological environments, the
distributions of past seismic activity and the latest research
information on earthquake prediction and prevention
need to be fully considered. We used past seismic activity
characteristics, seismic activity faults and seismic
Fig. 3 Seismic peak ground acceleration zonation map of China.
Legend
County boundary
China land boundary
Provincial capital cites
Peak ground acceleration (g)
0.05
0.10
0.15
0.20
0.30
≥0.40
<0.05
Table 1 Correlation between earthquake intensity and seismic loss.
Note: ** designates significance at 0.01 significance level (two-tailed test).
Pearson correlation coefficient Mortality Injury Homeless
Affected
population
Direct economic
losses
Building
collapse
Seismic magnitude 0.280** 0.262** 0.517** 0.295** 0.236** 0.322**
Seismic intensity 0.409** 0.392** 0.564** 0.374** 0.441** 0.468**
Table 2 Disaster-breeding environment data of Tangshan
municipal district.
County unit X1 X2 X3
Tangshan municipal district 11 Yes 0.20

5. XU Zhongchun, et al.: Quantitative Assessment of Seismic Mortality Risks in China 87
fortification levels to determinate earthquake occurrence
probability.
On the basis of manually interpreting history seismic
integrated intensities, seismic fault belt distributions
and seismic peak ground acceleration (Figs.1–3, Table
3), the following formula was adopted to calculate
earthquake occurrence parameters for every county. For
this formula to be effective, several principles needed to be
demonstrated, including that places with a strong history
of seismic intensity would have a higher probability of
future earthquake occurrence, places with seismic activity
fault belts would have higher earthquake occurrence
probabilities and places with high fortification levels
would have higher earthquake occurrence probabilities.
1 2 3
3
x x x
P
+ +
=
P is the earthquake occurrence parameter; x1 is the
seismic intensity parameter; x2 is the seismic fault belt
parameter; and x3 is the seismic peak ground acceleration
parameter.
The process of determining the parameters was as
follows: when X1≥A, x1=1;and when X1<A, x1=0. If
seismic activity fault belts went through the county, x2=1;
otherwise, x2=0. When X3≥A, x3=1; and when X3<A, x3=0
(shown in Table 4). X1 is history seismic intensity, X2 is the
distribution of seismic activity fault belts and X3 is seismic
peak ground acceleration. A is seismic intensity grates.
According to the corresponding relationship between
seismic peak ground acceleration and seismic intensity,
the parameter of seismic peak ground acceleration can
be interpreted (State Bureau of Quality and Technical
Supervision 2001). The three parameters were calculated
using the formula, and their values were between 0 and 1.
Using Tangshan municipal district as an example,
the method of averaging parameters of history seismic
integrated intensities, seismic fault belt distributions
and seismic peak ground acceleration was adopted to
characterize earthquake occurrence probability. This
method avoided the problem that earthquakes can’t
be predicted accurately. And earthquake occurrence
parameters in other counties can be calculated in this
method too.
Generally speaking, Seismic intensities of Ⅰ-Ⅳ could
not cause casualties, and intensity of Ⅶ rarely occur
in China. Therefore, we assessed quantitative seismic
mortality risks at intensities ranging from Ⅹ to Ⅺ .
3 Results
Based upon the established seismic vulnerability curve
and earthquake occurrence parameters, seismic mortality
risks were assessed at the county level using the improved
assessment model. The seismic mortality risks were
mapped to visually illustrate their geographic distribution,
and then the spatial patterns were analyzed (Fig. 4).
By analyzing the seismic mortality risk maps, it is
apparent that there are great differences between Chinese
counties at different intensity levels. With increasing
seismic intensity, there is a trend for increasing seismic
mortality risk (Table 5).
The spatial distribution patterns of seismic mortality
risks were studied and again there are large differences
among counties at the same intensity levels. In general,
China’s eastern and central regions were confronted with
higher risk than western regions.
Generally speaking, high-risk areas had the denser
population and the higher earthquake occurrence
parameters. They were scattered throughout most of
Shandong and Jiangsu, northern Anhui and eastern
Heilongjiang and Jilin, where the population is dense and
the environment is conducive to disasters.
Table 3 Comparison of seismic peak ground acceleration and seismic intensity.
Title Corresponding relationship
Seismic peak ground acceleration (g) ﹤0.05 0.05 0.10 0.15 0.20 0.30 ≥ 0.40
Seismic intensity ﹤Ⅵ Ⅵ Ⅶ Ⅶ Ⅷ Ⅷ ≥ Ⅸ
Table 4 Earthquake occurrence parameters from Tangshan municipal district.
Tangshan municipal district
Seismic intensity grate (A)
Ⅴ Ⅵ Ⅶ Ⅷ Ⅸ Ⅹ Ⅺ
Seismic intensity parameter (x1) 1 1 1 1 1 1 1
Seismic fault belt parameter (x2) 1 1 1 1 1 1 1
Seismic peak ground acceleration parameter (x3) 1 1 1 1 0 0 0
Earthquake occurrence parameter (P) 1 1 1 1 0.67 0.67 0.67
Table 5 Statistics of seismic mortality risks.
Seismic intensity Ⅴ Ⅵ Ⅶ Ⅷ Ⅸ Ⅹ Ⅺ
Seismic mortality risk 1 958 685 3 844 289 5 617 770 7 450 337 12 475 701 25 045 992 53 380 705

6. Journal of Resources and Ecology Vol.2 No.1, 2011
88
Risk-free areas showed patchy distributions nationwide,
and their distributing patterns were mostly unchanged.
They mainly occurred in Guangxi and Liaoning; western
Heilongjiang, Jilin, Jiangxi and Xinjiang Autonomous
Region; northern Hunan, Guizhou, Shaanxi, Inner
Mongolia and Fujian coastal area; and eastern Sichuan
Province. The county numbers and their percentages of
different risk grades at different seismic intensities are
shown in Table 6.
4 Conclusions and discussion
4.1 Conclusions
The quantitative calculation of seismic mortality risk is an
important problem, which had not been solved until now.
Based on the forming mechanism of seismic hazard risks,
we established a seismic risk assessment model, and then
assessed seismic mortality risks at the county level. The
following conclusions can be drawn from our analysis:
• New assessment methods for seismic mortality risk are
feasible, and can provide quantitative seismic mortality
risks for all counties. This can greatly improve the
original method of risk assessment grades.
• Based on data of seismic disasters from 1990 to 2009
in China, we established the vulnerability curve of
seismic mortality, which is the main base to assess
seismic mortality risks.
Table 6 Statistics of seismic mortality risks from V to XI.
Intensity County
Seismic mortality risk grades
0 ≤100
100–
1000
1000–
5000
5000–
10 000
10 000–
50 000
50 000–
100 000
﹥100 000 No data
Ⅴ
Number 0 146 1565 629 14 0 0 0 4
Percent 0 6.19 66.37 26.68 0.59 0 0 0 0.17
Ⅵ
Number 94 57 884 1224 80 15 0 0 4
Percent 3.99 2.42 37.49 51.91 3.39 0.64 0 0 0.17
Ⅶ
Number 390 18 521 1139 215 70 1 0 4
Percent 16.54 0.76 22.10 48.30 9.12 2.97 0.04 0 0.17
Ⅷ
Number 714 10 266 891 326 143 3 1 4
Percent 30.28 0.42 11.28 37.79 13.83 6.06 0.13 0.04 0.17
Ⅸ
Number 852 0 128 550 444 361 18 1 4
Percent 36.13 0 5.43 23.32 18.83 15.31 0.76 0.04 0.17
Ⅹ
Number 908 0 45 272 305 757 53 14 4
Percent 38.51 0 1.91 11.54 12.93 32.10 2.25 0.59 0.17
Ⅺ
Number 922 0 9 131 147 828 242 75 4
Percent 39.10 0 0.38 5.56 6.23 35.11 10.26 3.18 0.17
Fig. 4 Maps of seismic mortality
risk of intensities from V to XI.
(Data from Taiwan, Hong Kong,
Macau and Kinmen were not
available)
Seismic mortality risks
0
≤100
100–1000
1000–5000
5000–10 000
10 000–50 000
50 000–100 000
>100 000
Data not available
Ⅹ
Ⅺ
Ⅵ
Ⅴ
Ⅷ
Ⅶ
Ⅸ
Legend (Ⅴ–Ⅺ)

7. XU Zhongchun, et al.: Quantitative Assessment of Seismic Mortality Risks in China 89
• We used past, present and future disaster-breeding
materials to determine earthquake occurrence
probabilities. In order to calculate earthquake
parameters of all Chinese counties accurately, we
integrated history seismic intensities, seismic activity
fault belt distributions and seismic peak ground
acceleration.
• Seismic mortality risks were assessed quantitatively
at the county level using the model and the spatial
patterns were subsequently analyzed. Seismic mortality
risks for 2355 counties from V to XI were thoroughly
determined. According to the assessment results, the
total seismic mortality risk for an earthquake intensity
of the Ⅴ degree is 1 958 685, the Ⅵ degree is
3 844 289, the Ⅶ degree is 5 617 770, the Ⅷ
degree is 7 450 337, the Ⅸ degree is 12 475 701,
the Ⅹ degree is 25 045 992 and for the Ⅺ degree the
total seismic mortality risk is 53 380 705 (Table 5).
This study presented new methodology for risk analysis
and assessment of seismic disaster. The methodology
employed in this study can be applied to quantitative study
of risk from other disaster-bearing bodies (Chen et al.
1997), and natural disasters (Anbalagan 1996; Lantada et
al. 2010).
4.2 Discussion
Risk values this paper obtained should not be considered as
precise predictions of future loss but rather as estimations
of loss, which were assessed by using the risk model
according to the forming mechanism of seismic risk. The
assessment results are relatively rough, only considering
situations that there’s no prediction before earthquake or
public awareness of disaster prevention and reduction is
low.
In earthquake disasters, there are a number of factors
that affect the number of casualties, including seismic
intensity, occurrence time, anti-seismic performance of
buildings, regional population, public awareness of disaster
prevention and reduction, response and action capacity
for different individuals and rescue measures (Sun et al.
1993).Seismic casualties are the integrated result of many
factors. Only seismic intensity was considered in this paper
and a regression curve between mortality and intensity was
established to assess the risk. If the correction coefficients
of other factors can be determined, the assessment results
will be more accurate, and the number between risk and
actual seismic mortality will be much closed (Sun et al.
1993).
In order to determine earthquake occurrence
probabilities, most risk assessments are conducted based
on historical earthquakes reoccurring (Wang et al. 2005),
which are often incomplete. We used past seismic activity,
seismic activity faults and seismic fortification levels to
determinate earthquake occurrence probability because
they are more scientific and reasonable.
Although the assessment results were relatively rough,
they could meet the basic need for Chinese earthquake
prevention and reduction. We can identify areas with high
seismic mortality risk, and prioritize the allocation of
resources and the development of life insurance in these
areas. According to results, we can take some actions in
advance. For example, in these high-risk areas, we should
reduce population exposures and so on. Finally, we can
achieve the goal of reducing the number of future deaths.
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8. Journal of Resources and Ecology Vol.2 No.1, 2011
90
中国地震灾害人口死亡风险定量评估
徐中春1,2
，吴绍洪1
，戴尔阜1
，李开忠1,2
1 中国科学院地理科学与资源研究所，北京100101；
2 中国科学院研究生院，北京100049
摘要：基于地震灾害风险形成机理，在建立人口震害脆弱性曲线与确定地震发生参数的基础上，本文利用评估模型对我国
Ⅴ-Ⅺ地震烈度下各县域单元的人口死亡风险进行评估并分析其空间分布格局。主要研究内容有：（1）首次采用基于过去—现
在—未来的多方面地震孕灾环境资料来处理地震发生的可能性。具体综合历史地震综合烈度、地震活动断裂带分布、地震动峰值
加速度三方面来确定全国2355个县域单元的地震发生参数；（2）利用1990-2009年我国历史地震灾情数据，对地震烈度与人员死
亡率之间进行线性拟合，建立适合我国地震灾害风险评估的震害人口死亡脆弱性曲线；（3）利用震害风险评估模型对我国各县域
单元的人口死亡风险进行定量评估，并分析风险空间分布格局，彻底摸清Ⅴ-Ⅺ地震烈度下我国各县域单元的地震灾害人口死亡风
险。研究表明：在不同地震烈度下，我国广大的东、中部地区面临更高的风险，而西部的人口死亡风险相对较低。高风险区域呈
零星状分布于山东与江苏大部、安徽北部、黑龙江与吉林东部等人口分布较密集且孕灾环境发育完备的区域。而无风险区域在全
国范围内呈斑块状散布，分布格局基本保持不变。
关键词：地震灾害；风险评估；人口死亡风险；中国
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