Prognostic estimations of casualties caused by strong seismic impacts

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1 See discussions, stats, and author profiles for this publication at: Prognostic estimations of casualties caused by strong seismic impacts Article READS 97 2 authors, including: Jose Badal University of Zaragoza 120 PUBLICATIONS 1,246 CITATIONS SEE PROFILE Available from: Jose Badal Retrieved on: 11 May 2016

2 Bulletin of the Seismological Society of America, Vol. 92, No. 6, pp , August 2002 Estimation of the Expected Number of Casualties Caused by Strong Earthquakes by Elena Samardjieva and José Badal Abstract The human losses after strong earthquakes that occurred in the world during the twentieth century have been analyzed, and a quantitative model for a preliminary assessment of casualties is proposed. It consists of a correlation between the number of casualties and the earthquake magnitude as a function of population density. We tackle the distribution of the total number of casualties within areas of different macroseismic intensity. Prognostic estimations of the expected number of killed or injured people caused by a supposed strong earthquake in Andalucia (Spain), using the model based on worldwide data, are suggested. Prognostic estimations based on specific data about the Kanto Tokai (Japan) region are likewise given and compared with the number of casualties due to the 1995 Kobe (Japan) earthquake. In relation to the expected number of victims in areas affected by strong seismic impacts, we compute the casualty rate as the number of people killed divided into the inhabitants of a region and show its variation for different population density groups in the case of two extreme earthquake magnitudes. Introduction The most important aspect after a destructive, strong earthquake is to pay attention to the number of human losses, in particular, the numbers of people killed and of individuals injured who require special medical help. In this sense, a preliminary evaluation of the possible consequences of strong earthquakes in every seismoactive region is very important for prevention and risk reduction purposes. This topic is undoubtedly important but has not received much attention in the past, although different authors have tackled this problem. Kawasumi (1951) proposed measures of the earthquake danger and the expectancy of maximum intensity from data of historical earthquakes that occurred in Japan. Lomnitz (1970) proved a correlation between the time of day and the number of casualties due to big earthquakes in Chile. Ohta et al. (1983) established a model of the number of victims caused by an earthquake as a function of the number of completely destroyed houses. Christoskov and Samardjieva (1984) investigated the possible number of casualties in the course of destructive earthquakes in the world for the period More recently, Samardjieva and Oike (1992) completed this study with worldwide and Japanese disastrous earthquakes that occurred during the period Shebalin (1985) proposed a classification of earthquake danger by assuming that the expected number of deaths increases with the growth of the population in different nations in the world. Oike (1991) studied the relation between the number of killed or injured individuals and the earthquake magnitude, as well as the temporal variation of earthquake disasters in various countries (Oike and Hori, 1998). In this study, we analyzed the observed casualties after destructive earthquakes that occurred in the world during the twentieth century. We discuss the problem of devising a quantitative scale of earthquake disaster to estimate the number of killed or injured people after strong earthquakes. Our purpose is to put forward prognostic estimations of the size of the human losses in selected urban areas in the case of strong earthquakes. In general, the size of the macroseismic effect is proportional to the earthquake magnitude but varies according to many other factors, such as focal depth, source mechanism, seismic wave attenuation, geological structure and peculiarities of the affected zone, site effects, type and quality of construction, population density, way of life, and seismological knowledge of the people. We adopt an empirical approach to relate earthquake magnitude with the expected number of victims and, through the use of previous work, also with the expected number of injured persons. We present new expressions derived from an ample database of disastrous earthquakes in the twentieth century. To obtain a reliable evaluation, our equations combine the following parameters: earthquake magnitude, population density in different parts of the affected territory, and dimensions of the areas with different macroseismic intensity. Methods According to the available worldwide data during the twentieth century until 1999, almost half a thousand earth- 2310

3 Estimation of the Expected Number of Casualties Caused by Strong Earthquakes 2311 quakes resulted in more than 1,615,000 human victims. An Appendix at the end of this article collects all of the seismic events taken into account in our study. This large database (date, earthquake magnitude, affected region, and people killed) contains 478 rows (one per event). Events with either a focal depth 60 km or one or two victims were discarded. Sometimes a precise number of victims cannot be given because it was unknown for some earthquakes. For the 1908 Italian earthquake, for example, the catalog of the National Earthquake Information Center (NEIC) gives a number of deaths from earthquake and tsunami between 70,000 and 100,000; we used 70,000. For the big 1927 China earthquake, the NEIC number of victims is 200,000, but it is 80,000 according to the Japanese catalog (Utsu, 1990); we used 80,000. The NEIC number of deaths caused by the 1935 Pakistan earthquake is between 30,000 and 60,000; we assumed 30,000. The NEIC number of human victims caused by the 1948 Ashkhabad earthquake is 19,800, although a much greater number (119,000) is also suggested; we used the first number. For the 1976 New Guinea earthquakes, the catalog NEIC gives 242 casualties but points out that there were between 5,000 and 9,000 missing people and presumed deaths; we noted 6,000. These examples illustrate the difficulty of setting precise numbers for our database. The annual number of victims is given in Fig. 1 (NEIC catalog of earthquakes ; Ganse and Nerlson, 1981; Utsu, 1990). The greatest numbers of casualties (N k ) were recorded after the 1920 and 1976 China earthquakes, with magnitudes M 8.5 and N k 220,000, and M 7.8 and N k 242,800, respectively. The 1923 Japan earthquake, M 7.9, caused a high number of casualties, too, N k 142,807. Considering the obvious differences in the social conditions of life during the first half of the century and after that, we can divide the whole period into two parts: before 1950 and after. For the period , the total number of victims is 949,047, and the mean annual number of victims is about 18,609 per year, whereas for the period , these numbers are 666,621 and 13,605, respectively. The shortdashed line in Fig. 1 shows a decreasing tendency of the annual number of victims in the world after strong earthquakes. This estimation does not confirm the general impression of an increase in the number of casualties due to contemporary earthquakes. For instance, Shebalin (1985), in his investigation of the consequences of earthquake disasters in the world, assumed that the number of casualties is proportional to the growth of the population, and he suggested coefficients for reducing the observed number of victims to the population density in In the approach proposed by Christoskov and Samardjieva (1984) to estimate the possible number of casualties, it was assumed that the total number of human losses, N k, is mainly a function of the earthquake magnitude, M, and the actual population density, D, in the affected area. Taking into account only earthquakes at normal focal depth (h 60 km), the estimation of the number of victims is based on an equation of the type log N (D) a(d) b(d)m (1) k where the coefficients a and b are regression parameters depending on the average population density D of the affected area. An analogous relationship for the assessment of the number of injured people, N inj, was also suggested (Christoskov and Samardjieva, 1984). For convenience, the ratio N inj /N k was introduced and the following relation was obtained: log (N inj/n k) M (2) The correlation coefficient was r This relation will be used to estimate the expected number of injured people due to strong earthquakes. Another model to estimate the number of victims was suggested by Christoskov et al. (1990). It was asumed that the number of casualties decreases proportionally with the square of the epicentral distance, R, similar to the attenuation of the seismic energy, N 1/R 2. A factor W I, depending on the radii R I of the areas of intensity I, was introduced. For instance, in the case of observed intensities I VII, VIII, IX (MSK), the weighting coefficients W I are 2 2 WI 1/[RI j (1/R j )], j VII, VIII, IX (3) Then the number of people killed within the area of intensity I can be determined by the equation (I) Nk W I N k (D I) (4) where the value of N k (D I ) is estimated from equation (1) for the actual average population density D I in the area of intensity I. Finally, the total number of human losses is a sum of the values N k (I). Results The human losses for both the first half and the second half of the twentieth century have been studied separately. The particular conditions of each zone affected by a strong earthquake have been analyzed, including the seismogeological aspects and the social conditions. The use of a uniform approach in the selection and elaboration of the data makes it possible to study the tendency of disaster mitigation in this century and to compare the two halves of the century. To obtain a mean value and an estimate of the expected number of casualties, a number of extreme events were excluded from the regression. Some earthquakes in eastern China were excluded because of the extremely high population density, those in Turkey and Iran because of the old type of buildings, and other events because of the epicenter s location under a big city or offshore, and so forth. For the cases that we have called extreme events, a few remarks have been written explaining why they were excluded (see

4 2312 E. Samardjieva and J. Badal Figure 1. Annual number of human victims in the world caused by M 5 earthquakes during the twentieth century. Appendix). The exclusion of some extreme cases or big events was done for practical reasons and must be considered with caution: our purpose is to obtain as nonbiased an estimate as possible. As is well known, the size of the macroseismic effect depends not only on the earthquake magnitude but also on many variables, such as those mentioned previously. The combination of data of extreme cases does not seem to be the most adequate way to obtain a reliable mean value. In the case of the 1976 China earthquake, M 7.8, focal depth h 22 km, and 242,800 people dead, the high population density contributed to an increase in the number of casualties. This same reason contributed to a similar effect in the case of the 1985 Michoacan (Mexico) earthquake, M 8.1, focal depth h 28 km. This seismic event had its epicenter 350 km away from Mexico City, on the Pacific coast, but it caused an out-of-the-ordinary large intensity at Mexico City and was felt by 20,000,000 persons in an area of 825,000 km 2, with 9,500 people dead. Here there were clear site effects, which played a huge role in the damage. There is no distinction in principle between cases in which site effects play a huge role in the damage, but not so with data from earthquakes at short and intermediate distances. In contrast to the two previous examples that, in our opinion, could bias the final estimations, other cases were accepted in our database when they were not far from the statistically mean values in the world (normal focal depth h 60 km, mean population density D 160 people/km 2 ). In the case of the 1988 Armenia earthquake, for instance, M 6.8 and 25,000 people dead, site effects also played a huge role in the damage, and the number of victims corresponds to a higher-magnitude event. However, this seismic event has not been discarded in our work because it has a normal focal depth (h 10 km) and it occurred in a zone with a population density that falls into one of the density groups

5 Estimation of the Expected Number of Casualties Caused by Strong Earthquakes 2313 that we established in a subsequent section. (Utsu, 1990). As much again can be said with respect to another relatively small magnitude event that caused much more casualties than expected from the expressions presented here: the 1999 Colombia earthquake, M 6.1 and 1,185 people dead. This earthquake, like the prior event, has been included in the database because of its normal focal depth (h 17 km) and the population density of the affected zone. With this background, anyone can understand the degree of difficulty that a correct worldwide data selection involves. Regarding the case of Japanese data, for instance, we can say that considerations, such as aseismic building, have been taken into account. The standards law for aseismic construction in Japan was enacted in 1950, but the new aseismic design methods for construction were not enforced in some zones until This example illustrates well the difficulties of selecting earthquakes included in the database from those that were excluded. The detailed discussion of every extreme case would occupy many pages and is not a part of our goal. Earthquakes with epicenters on the coast or near it have been taken into account. In these cases, only a part of the macroseismic field inland provokes human losses. By considering the proportionality of inland and offshore areas with respect to the whole area of strong seismic impact, the theoretical number of casualties for these events has been recalculated, assuming that the number of victims would increase proportionally if the area of seismic impact had been located totally inland. For the correlation of earthquake magnitude with the number of victims, only events at normal focal depth (h 60 km) have been selected. Using the approach proposed by Christoskov and Samardjieva (1984) and taking into account the new data for the twentieth century, we have obtained regression equations of type (1) for various population density groups. We have applied a standard least-squares method and grouped the data according to the most frequently met density groups in the world: D 25, D 25 50, D , D , and D 200 people/km 2. The values of the coefficients a and b in equation (1) for the period and various population density groups are given in Table 1. Analogously, the results for the first half of the twentieth century ( ) obtained by the same method (Samardjieva and Oike, 1992) are also given in Table 1, which contains the respective correlation coefficients and error bounds to casualties. These 1 r errors allow us to assess the level of uncertainty implied in the predictive equation (1) and are not small, as expected, because of the large variety and dispersion of the data. The observed numbers of victims as a function of the magnitude of the earthquakes that occurred during the second half of the century ( ) are plotted in Fig. 2. This figure shows as many regression lines as fixed density groups we selected. The line corresponding to D 200 people/km 2 was obtained using only data of epicenters under a big city. On the basis of these results, we have built the Table 1 Regression Coefficients a and b in Equation (1) for the Periods and and Different Population Density Groups Population Density (people/km 2 ) a b r r a b r r D D D D D r, correlation coefficient for the linkage of the variables; r standard deviation. Figure 2. Log linear regressions based on worldwide data for the number of human victims caused by earthquakes in the magnitude interval for different population densities. nomogram shown in Fig. 3. The solid lines represent the correlation for earthquakes of the second half of the twentieth century ( ), whereas the dashed lines correspond to earlier earthquakes ( ) (Samardjieva and Oike, 1992). The regression lines linking magnitude with expected number of casualties for various population densities are not parallel in the semilog representation, neither for the period nor for the period (Table 1, Fig. 3). Thus, the number of human losses for both

6 2314 E. Samardjieva and J. Badal these aspects are not sufficient to preserve human life in the case of destructive earthquakes with magnitudes 7.0. In such cases, an important contribution to an increase in the number of casualties seems to be the modern way of life, which provokes undesirable secondary effects like fires, gas poisoning, and so forth. Prognostic Estimations Figure 3. Same log linear regressions as in Fig. 2, but with the periods (dashed lines) and (solid lines) plotted separately. the first half and the second half of the twentieth century seems to depend on population density beyond strict proportionality. In the magnitude interval, the correlation between the number of victims and the earthquake magnitude for certain categories of population density is based on mean observational values and provides an approximate estimation of the expected number of human losses. For the lowest population density (D 25 people/km 2 ), the observed number of victims before 1950 is clearly less than for the period after This result is perhaps due to a lack of data or deficient information concerning early instrumentally recorded earthquakes that occurred in regions with low population density. In general, just the opposite occurs: earthquakes of equal magnitude occurring in regions with the same level of population density cause fewer casualties at present than in earlier years. This is always valid for D 200 people/km 2 and also in areas with less population density for magnitudes 7.0. However, in the case of earthquakes of greater magnitude, the reverse situation is observed. This means that modern construction based on contemporary earthquake engineering, in which the quality of the materials used has improved, and with the greater knowledge people have about seismic phenomena contribute to moderate the earthquake disaster. Nevertheless, all of Andalucia Region, Spain The Iberian Peninsula is a region with moderate seismic activity. Badal et al. (2000), in their magnitude and spectral analysis, could gather data of only 18 felt earthquakes with an epicentral intensity VI (MSK) in the peninsula during the period WWSSN stations were introduced in Despite this, large historical earthquakes, with a maximum intensity I o VII (MSK), have occurred in the Iberian area. Figure 4 shows a map of the peninsula and adjacent areas with the epicenters of these earthquakes. The last destructive earthquake in Spain, the big earthquake of Andalucia (southern Spain), dated 25 December 1884, maximum intensity I o X(MSK), completely destroyed several small villages and caused 749 deaths. The epicentral area of this shock has not suffered a similar earthquake since then. Nevertheless, 100 years later, on 24 June 1984, an earthquake of maximum intensity I o VIII (MSK) occurred in the same area. The study of this seismoactive zone indicates that there is significant risk of occurrence of a large earthquake in the future (Vidal et al., 1989). High macroseismic intensities of degree VIII and IX (MSK) for a return period of 100 and 500 years, respectively, are expected (Payo et al., 1994). An objective assessment of earthquake hazard for a seismoactive region can be obtained from statistically significant observations. In the case of the Andalucian region, the data about the number of human losses after strong earthquakes are limited. On the other hand, the seismological and social conditions of this zone correspond to the statistically mean values in the world: normal focal depth h 60 km, expected maximum intensity I o IX, and mean population density D 160 people/km 2. That is why we use the model based on worldwide data for prognostic estimation of the earthquake consequences in this region. Let us suppose the occurrence of a seismic event of magnitude M 7 with the epicenter near to that of the big earthquake of Andalucia (estimated coordinates 37.0 N, 04.0 W; focal depth, h 15 km). The expected maximum intensity is I o IX (Willmore, 1979). To estimate the dimensions of the expected macroseismic field, we use the intensity attenuation curves of Payo et al. (1994). The elliptic form of the isoseismals according to the dominant horizontal fault displacement has the orientation of the observed macroseismic field (Vidal et al., 1989). Figure 5 shows the isoseismals (approximately between the cities of Málaga and Granada) plotted for degrees I VIII (semiaxes 55 and 85

7 Estimation of the Expected Number of Casualties Caused by Strong Earthquakes 2315 Figure 4. Map showing the epicenters (circles) of earthquakes that occurred during the period felt in the Iberian region with a maximum intensity I o VII. Figure 5. Expected isoseismals plotted for intensity degrees I VIII and I IX (MSK) caused by a hypothesized strong seismic impact of magnitude M 7 in Andalucia (southern Spain), at the same location (37.0 N, 04.0 W) of the 25 December 1884 earthquake. The population density is illustrated by quadrangles of 5 5 km (Atlas Nacional de España, 1991), and its observed maximum values correspond to the principal urban nuclei in the region. km) and I IX (semiaxes 25 and 45 km) on a schematic map of Andalucia (southern Spain), which correspond to the hypothetical strong seismic impact. The population density is also illustrated in this figure by quadrangles of 5 by 5 km (Atlas Nacional de España, 1991). Assuming that the number of casualties decreases proportionally with the square of the distance (Christoskov et al., 1990), the following ratio was used: (VIII) (IX) 2 2 N /N R /R (5) k k IX VIII

8 2316 E. Samardjieva and J. Badal where N k (VIII) and N k (IX) are the casualties occurring in the areas of radius R VIII and R IX affected by intensities VIII and IX, respectively. The expected total number of human losses N k is taken from the nomogram of Fig. 3, and it is (VIII) (IX) Nk Nk N k (6) From equations (5) and (6) it follows that and (IX) Nk [R VIII/(RVIII R IX)] N k (7) (VIII) Nk [R IX/(RVIII R IX)] N k (8) The results of these calculations are given in Table 2. First, the expected number of killed or injured people for each intensity (I IX and I VIII) have been obtained using the average population density D 160 people/km 2 for the whole area of strong seismic impact. More detailed calculations using a specific population density D 80 people/ km 2 in the area contoured by I IX and a density D 200 people/km 2 in the area covered by I VIII have also been obtained, and the results are likewise given in Table 2. For an average density of 160 people/km 2, we comparatively obtain higher values for the total number of dead and injured people: N k 1,800 and N inj 5,700. Obviously, our prognostic estimations apply only for epicentral areas affected by a relatively strong seismic impact on the basis of their population density. This introduces some uncertainties, which are inherent to the problem and can be expressed in numerical terms. Considering standard deviations of and (Table 1), the respective uncertainty intervals are 800 4,000 and 2,500 12,000. In contrast to those numbers, for densities of 80 and 200 people/km 2, we clearly deduce lower values for the total number of dead and injured people: N k 1,000 and N inj 3,100. Now, considering standard deviations of 0.30 and 0.34 (Table 1), the respective uncertainty intervals are 450 2,200 and 1,400 6,900. As shown, the results are strongly dependent on the population density in the areas of different seismic impact (contoured by different isoseismals). Other factors also contribute to the uncertainty, such as the lack of precision in the delineation of the macroseismic field. In the example analyzed, both prognostic estimates are an obvious consequence of the use of two different population densities, average and smaller than the average. We would like to stress that the way of distributing casualties in the various isoseismal regions is, of course, debatable. It is not the purpose of this article to discuss this procedure. In this sense, the approach for a prognostic estimate of the possible number of casualties in a region should be considered as an example. The use of precise values for population density would lead to more accurate prognostic estimates. Table 2 Numbers of Killed (N k ) or Injured (N inj ) People, Depending on Population Density, Estimated for the Hypothesized Seismic Event (M 7) in Andalucia (Southern Spain) D (people/km 2 ) I (MSK) N k N inj 160 IX 1,300 4,100 VIII 500 1,600 Total 1,800 5, IX 350 1, VIII 650 2,000 Total 1,000 3,100 Model earthquake M 7 (depth h 15 km) Andalucia region, Spain (37.0 N, 04.0 W) Kanto Tokai Region, Japan The history of worldwide earthquakes shows that the greatest seismic disasters have occurred in eastern Asia. The geographical distribution of the number of deaths by seismic disaster during the twentieth century is shown in Fig. 6 (Oike and Hori, 1998). Considering the large difference in social conditions of life in this region (extremely high population density, type and quality of buildings, antiseismic activities, etc.), a specific study based on equation (1) should be made. As another example, we have investigated the Kanto Tokai (Japan) seismoactive zone. Only in this zone, 18 earthquakes have resulted in a large number of victims during the last 100 years. In 1923, this region was affected by the great M 7.9 Kanto earthquake, which produced 142,000 human victims. The data for the Kanto Tokai region have been adjusted to expressions (1) and (2), and the following regression equations have been obtained: log N M k (9) (correlation coefficient) 0.74 log Ninj M (10) (correlation coefficient) 0.72 Because almost all of this region is urbanized with a high population density, we do not make any division into different density groups for the following estimates. Let us assume the occurrence of an earthquake of magnitude M 8 in the Kanto Tokai district (supposed coordinates 36.0 N, E; focal depth h 20 km). In agreement with the intensity expected throughout Japan (Kawasumi, 1951), the radius of the area affected by intensity I V (JMA), the lowest intensity at which casualties are expected, would be R 142 km. The radius of the area affected by intensity I VI would be R 73 km. Considering a population density D people/km 2 and using the nomogram based on worldwide data (Fig. 3), we obtain, by the same procedure as before, the results given in

9 Estimation of the Expected Number of Casualties Caused by Strong Earthquakes 2317 Figure 6. Geographical distribution in eastern Asia of the number of deaths caused by seismic disasters during the twentieth century (after Oike and Hori, 1998). The three largest circles correspond to the destructive 1920 and 1976 China earthquakes and to the disastrous 1923 Japan earthquake in the Kanto Tokai region. Table 3: N k 14,000 (5,600 35,000) and N inj 68,700 (27, ,000). However, if we repeat the process by considering the specific data for Kanto Tokai, the results, also shown in Table 3, are significantly lower: N k 5,500 (2,200 13,800) and N inj 14,000 (5,600 35,000). The last destructive earthquake in Japan of magnitude M w 6.8 (NEIC) and M 7.2 (JMA), the 1995 Kobe earthquake, caused 5,502 human victims and 36,896 injured people (catalogue of earthquakes NEIC, 1995). The expected number of casualties computed from different data sets in this second example emphasizes the bias that the approach based exclusively on worldwide data can introduce, in our case, an overestimated result. The solution of each problem depends on the available data, but the calculations based on specific information about the study zone must logically result in more precise estimates. Casualty Rate Finally, in relation to the expected number of victims in areas affected by strong seismic impacts, we refer to the concept casualty rate, that is, the number of killed people divided into the number of inhabitants of a region, for different population density groups. This is a parameter that can be easily determined for a seismically active zone when data about casualties caused by earthquakes with magnitudes ranging in a wide interval are available. In such a case, the variation of the casualty rate for distinct earthquake magnitude values can also be determined. We present in the next section a very simple example concerning a wide area of Table 3 Numbers of Killed (N k ) or Injured (N inj ) People Estimated for the Hypothesized Seismic Event (M 8) in Kanto Tokai (Japan) Relation Used I (JMA) N k N inj According to worldwide VI 11,300 54,000 data D V 3,000 14,700 (people/km 2 ) Total 14,000 68,700 According to VI 4,300 11,000 Kanto Tokai data V 1,200 3,000 Total 5,500 14,000 Model earthquake M 8 (depth h 20 km) Kanto Tokai region, Japan (36.0 N, E) 1,600 km 2 in Mexico, with a large urban concentration and different population densities, affected by large seismic activity entailing earthquakes with magnitudes up to 8. We do not compute new regression coefficients for this particular example. Our aim is rather to contrast the predictions obtained from our coefficients with a specific case. Starting from the values for a and b given in Table 1 for the period and taking into account events with magnitude 5 and 8, we estimate the casualty rate depending on different population density groups in that area of Mexico. Results are given in Table 4 and graphically in Fig. 7. They show a rather surprising consequence. For magnitude 5, the mortality rate is almost constant for all values of population density, but for magnitude 8, it is clear that the rate increases (almost one order of magnitude) with population density. For this last magnitude value, the rate in

10 2318 E. Samardjieva and J. Badal Table 4 Casualty Rate, Depending on Different Population Density Groups in an Area of Mexico with a Large Urban Concentration, for Earthquake Magnitudes 5 and 8 Population Density (People/km 2 ) Killed People/Casualty Rate Actual Density (People/km 2 ) a b Inhabitants Magnitude 8 Magnitude 5 D , E E-05 D , E E-05 D ,000 3, E E-05 D ,000 13, E E-04 D ,000 40, E E-04 The regression coefficients a and b of Table 1 have been used. technological equipment, high population density combined with a very strong seismic impact might lead to disastrous consequences for people who live in sites frequently afected by earthquakes. A correct interpretation of this point requires additional work to distinguish between undesirable evidence of a general character and mere appreciation valid for a particular case only. The conclusion emerging from this example is as disturbing as it is attractive and is worthy of being investigated. Concluding Remarks Figure 7. Variation of the casualty rate for different population density groups and two distinct values of earthquake magnitude (5 and 8). These values have been estimated with the coefficients a and b given in Table 1 for the second half of the twentieth century. a densely populous area (D 300 people/km 2 ) is or even more, which means that roughly 9% of the population could be killed as a consequence of such a seismic event. Even though the interpretation of a merely numerical problem that has to do with the lack of reliability for small earthquake magnitudes and small numbers of killed people is quite solid, an alternative hypothesis cannot be discarded. It seems that an increase in the casualty rate could be due to lifestyle or undesirable hazard induced by fires, floods, gas poisoning, toxic emissions, harmful radiation, and so forth. This second conjecture is more appealing to us, still more when regarding the results obtained before and shown in Figure 3. In effect, the log linear regressions for the period (solid lines) seem to support this conjecture, suggesting that it is more dangerous to live in a populous and modern city when large seismic events occur. The achievements in the matter of prevention and risk reduction supported by modern building technology and the high degree of earthquake engineering applied would be somehow or other balanced with a higher level of risk derived from greater human concentrations above all in urban areas. Without discarding pernicious and unavoidable effects of modern The present study provides a method for the evaluation of possible consequences of strong earthquakes in terms of human losses. The work has been developed (1) by leastsquares regression linking earthquake magnitude with the expected number of casualties; (2) from an ample database of disastrous earthquakes in the twentieth century; (3) for both the first half and the second half of the century separately; and (4) for the most frequently encountered density groups in the world. This correlation, analyzed in the magnitude interval, is based on statistically mean values and gives an estimate of the expected number of casualties. In regions with a high level of population density (D 200 people/km 2 ) earthquakes of equal magnitude cause fewer casualties at present than in the past. In the same way that the mortality rate from infectious diseases (smallpox, polio, etc.) has been reduced everywhere during the last century, the number of casualties due to earthquakes has been reduced in the same period. In this sense, the casualty rate has changed significantly in a century. Science, updated building codes, and modern technology have undoubtedly contributed to lessen the impact of earthquakes in densely peopled zones. The same can be said for areas with a lower population density and for earthquakes with magnitudes 7.0. Unfortunately, for important human concentrations in big cities, where building codes are not always strictly applied and construction materials of quality are not generally used, it seems that large earthquakes with magnitudes 7.0 still provoke a number of casualties similar to that in earlier years.

11 Estimation of the Expected Number of Casualties Caused by Strong Earthquakes 2319 Prognostic estimates based on semilog regressions apply only to epicentral areas affected by a relatively strong seismic impact on the basis of their population density. In such cases, a correct choice of the population density is essential for correct prognostication. Likewise, calculations based on specific data of the investigated areas must logically lead to more accurate estimations. The prognostic estimates we have presented for the Andalucia region in Spain and the Kanto Tokai region in Japan should be considered as average values and interpreted as the most probable values, unless housing standards are significantly upgraded. Unpredictable consequences or undesirable hazards induced by fires, floods, gas poisoning, toxic emissions, harmful radiation, and so forth, can appear. In any case, it should be noted that the specific peculiarities of each zone (seismogeological factors, type and quality of buildings, antiseismic activities, energy lines, lifestyle, etc.) can influence the size of the consequences of earthquakes. A more realistic evaluation of the prognostic elements could be obtained by taking into account all of these factors. The variation in the casualty rate for a very strong earthquake suggests an increase with population density, probably due to secondary effects of modern technological development and lifestyle, particularly in urban areas. Far from a fatalistic view of earthquake hazards, assuming the viewpoint nowadays adopted by the scientific community, in the sense that people are, in a greater proportion, killed by structures and not by earthquakes, we think that any advance in civil engineering should contribute to moderate earthquake disasters. Acknowledgments We would like to thank the staff of the Geophysical Observatory of Toledo, Instituto Geográfico Nacional (Spain), for the help and facilities given in the course of this work. We are indebted to all of those who supplied us original information for this study. We also thank Associate Editor Francisco Chávez-García and the reviewers Cinna Lomnitz and Mario Ordaz for their constructive comments and guidance during the preparation of the draft that led to significant improvement of the manuscript. The section titled Casualty Rate was inspired by the data supplied by Dr. M. Ordaz. The Dirección General de Investigación, Ministerio de Ciencia y Tecnología, Madrid, partially supported this research through the project REN C References Atlas Nacional de España (1991). Sección IV, Grupo 14b, Potenciales Demográficos, Instituto Geográfico Nacional, MOPT, Madrid, 8 9. Badal, J., E. Samardjieva, and G. Payo (2000). Moment magnitudes for early ( ) instrumental Iberian earthquakes, Bull. Seism. Soc. Am. 90, Christoskov, L., and E. Samardjieva (1984). An approach for estimation of the possible number of casualties during strong earthquakes, Bulg. Geophys. J. 4, Christoskov, L., E. Samardjieva, and D. Solakov (1990). Improvement of the approach in determining the possible human losses during strong earthquakes. Bulg. Geophys. J. 4, Ganse, R. A., and J. B. Nerlson (1981). Catalog of significant earthquakes 2000 B.C. 1979, World Data Group A for Solid Earth Geophysics, report SE-27, Boulder, Colorado. Kawasumi, H. (1951). Measures of earthquake danger and the expectancy of maximum intensity throughout Japan, as inferred from the seismic activity in historical times. Bull. Earthquake Res. Inst. 29, Lomnitz, C. (1970). Casualties and behavior of populations during earthquakes. Bull. Seism. Soc. Am. 60, NEIC ( ) Catalog of earthquakes, National Earthquake Information Center, U.S. Geological Survey, Department of the Interior, (last accessed 5 April 2001). Ohta, Y., N. Goto, and H. Ohashi (1983). An empirical construction of equations for estimating number of victims by earthquakes. Zisin II 36, Oike, K. (1991). A discussion on the relation between magnitude and number of deaths by earthquakes, Proc. of the Int. Seminar on Earthquake and Hazard Mitigation Technology. Tsukuba, Japan, Oike, K., and T. Hori (1998). History of earthquakes and seismic disasters in east Asia. Science 68, Payo, G., J. A. Canas, and J. Badal (1994). Seismic hazard and anelastic attenuation in the Iberian Peninsula, Proc. of the U.S.-Spain Workshop on Natural Hazards. Barcelona, Spain, Samardjieva, E., and K. Oike (1992). Modelling the number of casualties from earthquakes. J. Nat. Disaster Sci. 1, Shebalin, N. V. (1985). Regularities of the natural disasters (in Russian). Nauki o zemle, v. 11, Znanie, Moscow, 48 pp. Utsu, T. (1990). Table of worldwide disastrous earthquakes, Tokyo, 243 p. Vidal, F., G. Payo, G. Alguacil, F. de Miguel, and M. D. Romacho (1989). The earthquake of June , at the centennial of the destructive Andalucian earthquake of 1884, Proc. of the ESC Symposium on Calibration of Historical Earthquakes in Europe and Recent Developments in Intensity Interpretation. Instituto Geográfico Nacional, Madrid, pp Willmore, P.L. (editor) (1979). Manual of Seismological Observatory Practice. Boulder, Colorado. Geophysical Institute of the Bulgarian Academy of Sciences Acad. G. Bonchev str., bl Sofia, Bulgaria (E.S.) Physics of the Earth, Sciences B, University of Zaragoza Pedro Cerbuna Zaragoza, Spain (J.B.) Manuscript received 10 February 2001.

12 2320 E. Samardjieva and J. Badal Appendix List of Earthquakes that Caused Human Losses during the Last Century

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14 2322 E. Samardjieva and J. Badal