Improvement in the accuracy of expected seismic intensities for earthquake early warning in Japan using empirically estimated site amplification factors
© The Society of Geomagnetism and Earth, Planetary and Space Sciences (SGEPSS); The Seismological Society of Japan; The Volcanological Society of Japan; The Geodetic Society of Japan; The Japanese Society for Planetary Sciences; TERRAPUB. 2011
Received: 12 April 2010
Accepted: 10 December 2010
Published: 28 February 2011
The Japan Meteorological Agency (JMA) is able to process data for its earthquake early warning (EEW) procedure very quickly by employing a seismic intensity expectation method that uses simplified strong motion characteristics. To represent one of these characteristics, the site amplification factor at the seismic intensity station, JMA uses ARV (amplitude ratio of peak ground velocity at the ground surface relative to the engineering bedrock of average S-wave velocity 700 m/s) based on topographic data. As a potential substitute for the ARV, we investigated a station correction based on empirical site amplifications obtained from recent observed seismic intensity data. When estimating this station correction, we removed an effect of the abnormal seismic intensity distribution of deep subduction-zone earthquakes and applied a correction for the earthquake source term to the attenuation relation. Station corrections were obtained for 1258 stations, representing about 30% of all current seismic intensity stations. The correlation between our station corrections at the stations and the site amplification based on topographic data is weak (correlation coefficient = 0.30). The error of the expected seismic intensity was improved by 15% by replacing ARV with the station correction.
Observed strong motion characteristics of earthquakes generally consist of source effects, wave propagation path effects, and site effects (e.g., Iwata and Irikura, 1986). For accurate evaluation of strong motion taking into account these effects, an understanding of the fault rupture process, the attenuation structure of seismic waves and, especially, the local soil conditions is indispensible. Strong motion varies greatly and depends on the degree of amplification by the subsurface ground structure, even for events of the same magnitude and with the same seismic wave propagation paths.
The Japan Meteorological Agency (JMA) began providing earthquake early warning (EEW) services to the general public on 1 October 2007 with the aim of mitigating seismic disasters (Hoshiba et al., 2008; Kamigaichi et al., 2009; Doi, 2010). An EEW notification includes information on the expectation of strong ground motion immediately after an earthquake, but before destructive strong motion arrives, by processing real-time data obtained at one or a few seismometers positioned near the source region. To achieve such rapid processing, the JMA employs an expectation method that uses three major factors controlling strong motion: (1) the earthquake magnitude M estimated from P-wave amplitude to represent source effects; (2) the attenuation relation to model wave propagation path effects; (3) a site amplification factor, ARV (amplitude ratio of peak ground velocity at the ground surface relative to the engineering bedrock of average S-wave velocity 700 m/s) for site effects.
The technique for quick determination of the hypocenter and M in the JMA EEW uses waveform data collected from about 200 accelerometers of the JMA seismometer network (Harada, 2007) and from about 800 high-gain seismometers (velocity-type sensor; natural period is 1 s) of the Hi-net network of the National Research Institute for Earth Science and Disaster Prevention (NIED) (Okada et al., 2004). Strong motion intensity expectation in the JMA EEW is performed on seismic intensity measurements on the JMA scale. The expectation of seismic intensity is based on the empirical methods of Si and Midorikawa (1999), Matsuoka and Midorikawa (1994), and Midorikawa et al. (1999), which are based on the hypocentral distance, focal depth, M, and site amplification factor.
The instrumental observation of seismic intensity started in 1990s in Japan. The first seismic intensity meters were installed in 1991, and their function was enhanced in 1996 (JMA, 1996). In addition, local governments and NIED started to install seismic intensity meters in 1996. At the present time, at least one seismic intensity meter is installed at each village, town, ward, and city in Japan. These meters are relatively densely deployed in populated areas and sparsely at mountain areas and there are currently about 4,200 stations throughout Japan (JMA, 2009; JMA, http://www.seisvol.kishou.go.jp/eq/intens st/index.html). The data on instrumental seismic intensity is compiled by JMA according to the method explained on the JMA website (http://www.seisvol.kishou.go.jp/eq/kyoshin/kaisetsu/calc_sindo.htm), the Headquarters for Earthquake Research Promotion website (http://www.hp1039.jishin.go.jp/eqchreng/at2-4.htm), and Hoshiba et al. (2010). The observed seismic intensity at each station provides information that helps determine the initial response to disasters immediately after an earthquake. For example, when the observed instrumental seismic intensity is ≥5.5 on the JMA scale, an emergency assembly team conference is held at the Prime Minister’s office.
There are two categories in the JMA EEW notification, namely, “warning” and “forecast”. Japan is divided into 187 regions depending on prefectural borders, and the category of “warning” or “forecast” is issued on a per-region basis (Doi, 2010). Between two and 95 seismic intensity meters have been installed in each region. The seismic intensity is expected at the locations of the intensity meters, and the category of “warning” or “forecast” is issued depending on the expected intensities. When the expectation at one or more stations is ≥2.5 on the JMA scale (or M is estimated to be >3.5), a “forecast” is issued for the region; when it is ≥4.5, a “warning” is issued (Hoshiba et al., 2008). Note that, under the current operating conditions of the JMA EEW, the expectation of seismic intensity is performed for those locations where seismic intensity meters are installed (and not for whole area, including the locations where intensity meters are not installed). The site amplification factor at the site is an important factor in the expectation of the intensity. In the current system, ARV estimated from topographic data (Matsuoka and Midorikawa, 1994) is used to represent the site factor. However, topographic information may not be the best way to obtain site amplification estimates at each station. Additionally, most seismic intensity stations do not collect enough information on local soil conditions, such as borehole logs in the upper engineering bedrock, for site amplification to be estimated.
In the study reported here, we explored the possibility of using a station correction factor at the seismic intensity station, namely, an empirical site amplification estimate derived from peak ground velocity converted from seismic intensities observed in recent years, instead of the site amplification factor ARV based on topographic data. A number of studies that a station correction were estimated from comparison of the observed seismic intensities and the expected values by the attenuation relation. Fujiwara et al. (2007) estimated the station correction based on seismic intensity using the attenuation relation of seismic intensity; the results showed a weak correlation between the station correction at the sites and the site amplification based on topographic data. Kiyomoto et al. (2010) used the attenuation relation of JMA EEW’s logic to estimate station corrections; but only at 5% of all stations. Our overall aim is to improve the accuracy of the expected seismic intensity of JMA EEW at the locations where seismic intensity meters have been installed. We therefore used the attenuation relation of JMA EEW’s logic to estimate station corrections and defined the station correction as the amplification factor of peak ground velocity, similarly to the ARV, in order to be able to directly apply it to JMA EEW’s logic. As these station corrections are based on observed seismic data, they may improve the accuracy of seismic intensity expectation at the sites. We applied the station correction to expected seismic intensity and evaluated it as a replacement for ARV. All of the logarithms described herein are common logarithms.
2. Method for Estimation of Station Corrections
3.1 Basic dataset
Seismic intensity is recorded by about 4200 seismic intensity meters installed throughout Japan by local governments, NIED, and JMA, and their data are transmitted to the JMA. JMA typically reports a seismic intensity distribution within 2–5 min after the occurrence of earthquake. Hypocentral parameters including location and Mj, called a JMA-unified catalog, are determined by JMA. The observed seismic intensities and a JMA-unified catalog for each event are compiled in the database (Ishigaki and Takagi, 2000). In this study, we used the database of events of Mj ≥ 4.0 from May 1996 to April 2009. Focal depth and hypocentral distance were limited to shallower than 120 km and within 300 km, respectively, which is the same coverage as the dataset used in the regression analysis of the attenuation relation of Eq. (2). The earthquakes whose fault distance was estimated from the fault model are shown in Table 1. The fault models are mainly based on source inversion analysis by the Geospatial Information Authority of Japan (GSI). For those earthquakes not listed in Table 1, the fault distance was estimated using the empirical relation in Step 2 of Section 2. For Mw, we used the values determined by the NIED F-net (Okada et al., 2004) moment tensor inversion (http://www.fnet.bosai.go.jp/) during or after 1997 and by the Harvard University CMT inversion (http://www.globalcmt.org/) in 1996, except for the analysis in Sections 5 and 6.
3.2 Lower limit of observed seismic intensity
If we estimate the station corrections from PGVobs/PGV700exp, including the data from stations at large distances from the fault, the station corrections of the latter may be larger than the real values. Therefore, we excluded the data of larger fault distances at sites at which observed seismic intensity is <2.5 (Fig. 4). We used earthquakes that had at least five seismic intensity observations.
3.3 Distribution of data
4. Estimation of Station Correction
4.1 Correction of abnormal seismic intensity distribution
4.2 Correction of source term
Coefficient d in Eq. (2) represents the offset value of different earthquake types. The value of d for an intra-slab earthquake, 0.12, is much larger than the values for inland crustal and inter-plate earthquakes. Si and Midorikawa (1999) considered that the larger ground motion of intraslab earthquakes is caused by a high stress drop at the source. Asano et al. (2004) showed that stress drops on the asperities of shallow intra-slab earthquakes that generate strong ground motions are higher than those of inland crustal earthquakes. To reduce the difference in PGV700exp caused by different earthquake types, an offset correction is needed for the attenuation relation. For those earthquakes much deeper than the plate boundary, such as a focal depth >80 km, we can identify the earthquake type as the intraslab without focal mechanism, and we can also easily identify the earthquakes shallower than about 30 km far away from plate boundary as the crustal earthquakes. It is, however, difficult to identify the earthquake types for shallow earthquakes near the plate boundary. We assumed, therefore, that the adjustment of M using Eq. (10) not only reduces the inter-event variance but also has virtually the same effect as the offset correction of different earthquake types.
5. Characteristics of Station Corrections
6. Accuracy of Expected Seismic Intensity
In the previous section, we demonstrated that simply the replacement of ARV by empirically estimated station corrections leads to an improvement in the accuracy of expected seismic intensities for the current JMA EEW, even when other empirical relations (Eqs. (1), (2), (3), (4), and (6)) are unchanged. In addition to the improvement of site factors, it is expected that the modification of other empirical relations will lead to an improvement in accuracy. In this section, we discuss the improvement in the accuracy of expected seismic intensities through enhancement of the other empirical relations.
In this study, we focus on the improvement of accuracy of expected seismic intensities for the current JMA EEW through replacing ARV by empirically estimated station correction, even when other empirical relations (Eqs. (1), (2), (3), (4) and (6)) are not changed. Therefore, the station correction was estimated using the same empirical relations that underlie the JMA EEW’s logic. In addition, when estimating station corrections, we applied corrections of the abnormal seismic intensity distribution and source term for Eq. (2) in order to reduce the effects of factors other than site amplification. As a result, the error in expected seismic intensity was improved using our station correction. However, when estimating station corrections, we had to limit observed seismic intensities to those ≥2.5, mainly to reduce the distance dependence of PGVobs/PGV700exp. To reduce these residuals between the empirical relations and observed data, a new empirical relation should be regressed from recently observed data. Iwakiri et al. (2009) suggested that the attenuation relation of seismic intensity with Mj (Matsusaki et al., 2006) showed no dependence on fault distance within 100 km for recently observed data. In the future, when such a new empirical relation is introduced in the JMA EEW, it would therefore be possible to improve the accuracy of the expected seismic intensities. When estimating the station correction, we adjusted M by using observed seismic intensity to reduce the variance of the attenuation relation as described in Section 4. However, in the current JMA EEW algorithm, M is not estimated by using observed seismic intensity. Yamamoto et al. (2008) proposed using the observed seismic intensity itself to estimate M and showed that this method reduced the errors of estimated seismic intensities compared with use of Mj. In the JMA EEW algorithm, the accuracy of expected seismic intensity may be improved if M is estimated by observed seismic intensity.
Fujiwara et al. (2007) showed that the site amplification based on topographic data correlates weakly with the empirical site amplification estimated from the attenuation relation of K-NET (strong motion network operated by NIED). In Fig. 14, it is also shown that the correlation between our station corrections at the stations and the site amplification based on topographic data is weak. The errors of expected corrections for most earthquakes. These may suggest that ARV does not always represent site amplification just under the station, as the topographic data are represented by a 1-km square mesh cell. It may also be that ARV contains uncertainty stemming from the relation between AVS and ARV used in estimating ARV from topographic data.
As shown in Fig. 11(f), we found relatively large strong motions near the fault. One of the possible reasons for this is that strong motions observed near the fault may arise from factors in addition to site amplification. Midorikawa (2009) proposed that large strong motion variations near faults for recent inland crust earthquakes are caused by fault rupture propagation effects or the heterogeneous distribution of asperities. For example, observed seismic intensities for the 2004 Mid Niigata earthquake were relatively high on the hanging-wall side of the reverse fault (Midorikawa, 2006) and can be explained by the hangingwall effect (Abrahamson and Somerville, 1996). If we clearly find that strong motions observed near faults show a misfit to the attenuation relation for any other reason than site amplification, these data should not be used for estimation of the station corrections. However, it is sometimes difficult to identify whether the reason for the misfit of each earthquake is site amplification or not. It is also hard to fix the range of fault distance corresponding to misfit for each earthquake. Thus, we do not limit the range of fault distance near the fault when adjusting M and estimating the station corrections.
Kiyomoto et al. (2010) estimated site factors using observed seismic intensities ≥3.5 with the attenuation relation of Eq. (2). These researchers also conclude that replacing ARV by empirically estimated site factors improves the accuracy of seismic intensity expectation. The difference between our calculations and those of Kiyomoto et al. (2010) is that we reduced the uncertainty of station corrections by introducing the adjustment of M and the correction of abnormal seismic intensity distribution; we also raised the number of stations where station corrections are estimated by using an observed seismic intensity of ≥2.5.
Hoshiba et al. (2010) showed that even when the path factor and site amplification factor are appropriately evaluated, the uncertainty is expected to be 0.29 units on the JMA intensity scale if Mj is used as the index of the source factor, which is the intrinsic uncertainty limit for expected seismic intensity in the current JMA EEW algorithm. The expectation error in this study was 0.58 intensity units even when the station correction was used as the site amplification factor. Such a large expectation error would be due to the variance of attenuation and other empirical relations when estimating station corrections and expecting seismic intensities.
For present seismic intensity expectation of the JMA EEW, the effect of soil non-linearity is not included. The soil non-linearity during strong motion appears at more than about 15 cm/s of PGV (Midorikawa, 1993). On the other hand, the soil non-linearity on PGV is very small compared with peak ground acceleration (Midorikawa and Ohtake, 2003; Fujimoto and Midorikawa, 2006). Our station correction does not include the soil non-linearity. In the future, when we consider the soil non-linearity for large PGV, our station correction may be improved.
There has been a discussion of whether seismic intensity at each station represents typical strong motion around the area of the station. The seismic intensity itself indicates observationally the characteristic of ground motion at the station only (information of the point), but it is also required to be representative—to some extent—of the area around the station from a point of view of rapid estimation of possible damage to the area (representative of the area). Because of this requirement, the JMA has been surveying observed data and the installation environment to determine whether the station can be considered to be a properly representative observation site of the area (JMA, and Fire and Disaster Management Agency, 2009). When the station is identified as an inappropriate site, the seismic intensity meter is moved to another site or is not longer used as an observation site (when it is moved, new station ID is applied). In this paper, we discussed the improvement in the expectation of seismic intensity by replacing ARV by an empirically estimated station correction. Our purpose is to improve seismic intensity expectation at the locations installed seismic intensity meters. This improvement may be applicable only at the observational sites, but our analysis suggests that the expectation of ground motion in the EEW can be improved at locations where a seismic observation has been made, as compared with the locations where only topographic data are available.
To identify a more accurate solution for site effects than that provided by the site amplification factor ARV based on topographic data in the JMA EEW algorithm, we estimated station corrections by comparing the attenuation relation with observed seismic intensities from recent earthquakes. We applied a correction for the abnormal seismic intensity distribution and the adjustment of M. Station corrections for 1258 stations were obtained, which is about 30% of the national seismic intensity network. The correlation between station corrections and the site amplification based on topographic data is weak. The RMS accuracy of expected seismic intensities for the most recent 4 years was improved by 15% by using station corrections estimated from data of the previous 8 years, which means that the simple replacement of ARV by empirically estimated station corrections leads to an improvement in seismic intensity expectation even when other empirically relations (Eqs. (1), (2), (3), (4), and (6)) remain unchanged. Relatively large RMS errors in the station correction, however, persist near faults, which are probably explained not only by site amplification factor but also by fault rupture effects.
We are grateful to Dr. Maren Boese and an anonymous reviewer for their valuable comments and suggestions. We also thank the editor Dr. Tatsuhiko Hara for his valuable comments. Our seismic intensity dataset was recorded at seismic intensity meters operated by local governments, NIED, and JMA. For hypocentral information we used the JMA seismic catalog unified seismic data from NIED, Hokkaido University, Hirosaki University, Tohoku University, University of Tokyo, Nagoya University, Kyoto University, Kochi University, Kyushu University, Kagoshima University, the National Institute of Advanced Industrial Science and Technology, the Tokyo Metropolitan Government, the Shizuoka Prefectural Government, the Kanagawa Prefectural Government, the City of Yokohama, the Japan Marine Science and Technology Center, and JMA. Figures were prepared using GMT (Wessel and Smith, 1991)
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