Development of On-Site Earthquake Early Warning System for Taiwan

Taiwan is located between Euro-Asian and Philippines tectonic plates on the Pacific Earthquake Rim; therefore, Taiwan has suffered from the threatening of moderate earthquakes for a long time.The earthquake usually caused tremendous damages to human beings and these irreversible damages include loss of human lives, public and private properties, as well as huge adverse economic impacts.It is very difficult to avoid the damages caused by earthquake due to its widely destructive power.However, if people can receive the warning for the coming of the earthquake even by only a few seconds, the damages can be reduced due to possible appropriate reaction.The earthquake early warning system (EEWS) makes it possible to issue warning alarm before the arrival of Swave (severe shaking) and then to provide sufficient time for quick response to prevent or reduce casualty and damages.The idea of EEWS was originated in the U.S. (Cooper, 1868) based on the principle that transmission of the electronic signal is faster than the earthquake wave, and the typical research project goes ahead mainly in the California.Up to now, there are three types Earthquake Early Warning System (EEWS).The first type is based on the earthquake locating of local seismometer network systems, the second type is based on an on-site warning of a single seismometer, and the third type is a mixed combination of the first two types.The first type EEWS is a traditional seismological method which locate earthquake, determine magnitude using local seismometer network readings then estimate strong ground motion for other sites.In 1985, the very beginning of Personal Computer (PC) era, Heaton proposed a seismic computerized alert network model which will provide shortterm warning (tens of seconds) for large epicentral distance region while a major earthquake happen.In Japan, Prof. Hakuno showed an idea of the earthquake early warning at an earlier stage.Also, JR's UrEDAS (Nakamura, 1988) is famous for their practical system.However, most seismic networks in the world cannot reach such goal.During the 1994 Northridge, 1995 Kobe earthquakes, seismic center took 30 minutes to hours to locate earthquakes.In 1999 921 Chi-Chi Taiwan earthquake, the critical information was www.intechopen.comEarthquake Research and Analysis -New Frontiers in Seismology 330 determined within 102 seconds.Since then, this type EEWS become mature and applicable.In 2007, Japan announced to public the first EEWS system in the world that can commercially provide earthquake warning information before the large S-wave amplitude arrives.The Real-time Earthquake Information by JMA is based on the source information as a point source and therefore the accuracy of the predicted ground motion is limited especially for a large scale earthquake.However, the limitation of first type EEWS produces a large blind zone where no warning will be received before S-wave arrives.Therefore the second type EEWS (on-site) is designed for such region.The third type EEWS is a hybrid use of regional and on-site warning methods.Although there is no real practical example, this is a reasonable research direction because the limitations of the first two types EEWS is somehow complementary.The regional EEWS is accurate but slow, the on-site EEWS is fast but less accurate.In this chapter, the development of the onsite EEWS for Taiwan is introduced.As part of the total solution of seismic hazard mitigation, an on-site earthquake early warning system (EEWS) has been developed for Taiwan.It provides time-related information including the magnitude of the earthquake, the expected arrival time of strong shaking, the seismic intensity and the peak ground acceleration (PGA) of the shaking, the dominant frequency of the earthquake and the estimation of structural response.The development of the on-site EEWS is divided into 2 stages.The 1 st stage provides a basic prediction of the earthquake, and in the 2 nd stage the response of the structure is estimated.In the 1 st stage, the P-wave predicated PGA method and neural networks were used to model the nonlinearities caused by the interaction of different types of earthquake ground motion and the variations in the geological media of the propagation path, and learning techniques were developed for the analysis of the earthquake seismic signal.The earthquake characteristics (PGA, amplitude, arriving time and dominate frequency… etc.) were then predicted at stage I.In the 2 nd stage, two different approaches are used to satisfy the different demands for the rapid estimation of structural responses.Both modules can estimate the structural response rapidly using the output of the first stage.This rapidestimation of structural response modulus can do the estimation online in a very short time.The user can get more information about what will happened in the coming earthquake.For different type of usage, two different modules are developed.The general modulus, which only uses the common data of the structure (height, structure type, floor, address …etc), is proposed to provide a low-cost, general-application and rapid estimation of the structural responses.For the user who needs more accurate and detail estimation of structural response, such as the hi-tech facilities, hi-raised building, power plant …etc.The customized modulus (scenario-based response predictor) provides a more accurate and detailed structural response estimation.Moreover, it can connected to the automatic control system, do the adequate decision under different levels of structural responses.With this customized modulus, the economic loss will be dramatically reduced.In order to build the two rapid estimation of the structural responses modulus, a wide range of real structural response data are needed.Only with these data, both modulus can be generated and verified.In this study, the Tai-Power building is used as the target structure; the refined FEM model is build by using FEM analysis software PISA3D.The recorded data of the structural responses from the CWB are used to refine the FEM model.After that, more than 50 on-site data and 200 free-field data are used as inputs in the FEM analysis.All the simulated structural responses from the FEM are collected into the database.