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One decade after the 1999 Chi-Chi earthquake, central Taiwan experienced more strong ground shaking [Central Weather Bureau (CWB), intensity VII] induced by a ML 6.2 earthquake on 5th November 2009. This earthquake occurred in the Mingjian Township of Nantou County, only 12 km southwest of the Chi-Chi earthquake epicenter. The broadband micro-earthquake monitoring network operated by the National Center for Research on Earthquake Engineering (NCREE) observed numerous aftershocks in the five days following the mainshock. The relocated aftershocks and the mainshock focal mechanism indicated a NE-SW striking fault dipping 60° toward the northwest. This fault plane is inside the pre-Miocene basement and the rupture extends from the lower crust to 10 km depth just beneath the basal décollement of the thin-skinned model that is generally used to explain the regional tectonics in Taiwan. The fault plane is vertically symmetrical with the Chelungpu fault by the basal décollement. The NW-SE compressive stress of plate collision in Taiwan, as well as the deep tectonic background, resulted in the seismogenic structure of the Mingjian earthquake at this location. [ABSTRACT FROM AUTHOR]

The western coastal plain of Taiwan borders the Western Foothills, which is a fold-and-thrust zone and one of Taiwan's major seismic zones. Earth quakes from the Western Foothills are a frequent cause of disaster in the western coastal plain. In this study, array measurements of microtremors were conducted at seven sites throughout the western coastal plain. At each site, four arrays (S, M, L, and XL arrays) of different sizes were considered. The largest radius of each array ranged from 32 m to 1 km. The Maximum Likelihood Method of Frequency-Wavenumber (F-K) analyses was used to obtain phase velocity dispersion curves. After the inversion of the dispersion curves with the initial models decided by Genetic Algorithm (GA) searching, shallow S-wave velocity structures for the western coastal plain were estimated. The S-wave velocity structures were estimated from the surface to a depth of 3 km. Above a depth of 0.4 km, they increased gradually under all sites. The first main interfaces were between 0.4 to 0.75 km in depth, and the average velocity at all sites increased sharply from 0.808 to 1.289 km sec-1. This interface seems to be the lower boundary of the thick unconsolidated sediments. The second main interfaces were between 0.9 to 1.75 km and the average velocity increases from 1.289 to 1.703 km sec-1. The third main interfaces were between 1.15 to 2.55 km and the average velocity in creased from 1.703 to 2.45 km sec-1. The depths of the last two main interfaces corresponded to the tops of the Pliocene and upper Miocene formations, respectively, as identified by seismic surveys and exploration wells. Further, the average S-wave velocities of the Plio-Pleistocene, Pliocene, and Miocene formations were estimated in this study. The existence of the Peikang Basement High, which is a prominent barrier in the area between the pre-Miocene and Neogene affects the features of these two interfaces. The depths vary apparently with the distances between the sites and the top of the Peking Basement High; i.e., Penghu Island. Furthermore, the complex structures near the Yichu hinge fault make the S-wave velocities of deeper structures under the Yijhu site higher than those under other sites based on the plane-layer assumption of the F-K analysis. [ABSTRACT FROM AUTHOR]

In the Tatun mountain area of northern Taiwan are two strong motion stations approximately 2.5 km apart, TAP056 and TAP066 of the TSMIP network. The accelerometer at station TAP056 is often triggered by earthquakes, but that at TAP066 station is not. Comparisons of vertical and horizontal peak ground accelerations reveal PGA in the vertical, east-west, and north-south components at TAP056 station to be 3.89, 7.57, and 5.45 times those at station TAP066, respectively. The PGA ratio does not seem to be related to earthquake source or path. Fourier spectra of earthquake records at station TAP056 always have approximately the same dominant frequency; however, those at station TAP066 are different due to different sources and paths of different events. This shows that spectra at TAP056 station are mainly controlled by local site effects. The spectral ratios of TAP056/TAP066 show the S-wave is amplified at around 8 ∼ 10 Hz. The horizontal/vertical spectral ratios of station TAP056 also show a dominant frequency at about 6 and 8 ∼ 10 Hz. After dense microtremor surveying and the addition of one accelerometer just 20 meters away from the original observation station, we can confirm that the top soft soil layer upon which the observation station is constructed generates the local site response at station TAP056. [ABSTRACT FROM AUTHOR]

Ambient noise is typically used to estimate seismic site effects and velocity profiles instead of earthquake recordings, especially in areas with limited seismic data. The dominant Horizontal to Vertical Spectral Ratio (HVSR) frequency of ambient noise is correlated to Vs30, which is the average S-wave velocity in the top 30 m. Vs30 is a widely used parameter for defining seismic amplification in earthquake engineering. HVSR can detect the vertical discontinuity of velocities, that is, the interfaces between hard bedrock and soft sediments. In southwestern Taiwan most strong motion stations are located in the plains and show a dominant frequency lower than 3 Hz. Several stations near the coast have low dominant frequencies of less than 1 Hz. The dominant frequencies are higher than 4 Hz at piedmont stations. The stations in the mountains with dominant frequencies over 8 Hz are typically located on very hard sites. This study analyzed the HVSR characteristics under different seismic site conditions considering the Vs30 from previous study (Kuo et al. 2012). The result implies that HVSRs are a better tool than Vs30 to classify the sites where bedrock is deeper than 30 m. Furthermore, we found a linear correlation between Vs30 and dominant HVSR frequency which could be used as a proxy of Vs30. The Vs30 map in this area was derived using the Engineering Geological Database for Taiwan Strong Motion Instrumentation Program (EGDT). The comparable distribution pattern between the dominant frequency and Vs30 demonstrate that HVSR can recognize S-wave velocity properties at the shallow subsurface. [ABSTRACT FROM AUTHOR]

Two consecutive large earthquakes eight minutes apart occurred offshore of Hengchun, Pingtung County of Taiwan at night on 26 December 2006. The seismic intensity in the Kaohsiung and Pingtung areas reached 5 according to the Central Weather Bureau's intensity scale making them the strongest earthquakes in the Hengchun area in a century. These earthquakes were felt throughout Taiwan. Because the earthquakes were located not far offshore, seismic instruments at the Taipower's Nuclear Power Plant No. 3 (NPP3) registered their largest motions since recording commenced at the site. As a result, unit 2 of NPP3 was shut down while Unit 1 remained operating at full capacity. Other damage that occurred during the Earthquake Doublet included building collapse, rock falls, structure and non-structural damage to buildings, fire, and damage to utilities such as gas, electricity, and telephone lines; liquefaction was also noted. In this paper, the shakemaps based on the source parameters and real-time observations were predicted and compared with the recorded shakemaps. This analysis shows our shakemap prediction model to be extremely useful in predicting what emergency response units can expect after a large damaging earthquake. Strong motion data near a collapsed building was used to calculate the response spectrum and compared with the building codes to check the reason of the building collapsed. This study presents the damage that occurred during this event and the results of the seismic assessment to discuss why so much damage occurred in the southern Taiwan area. [ABSTRACT FROM AUTHOR]

Peak ground acceleration (PGA) can be used to estimate the seismic intensity. However, using P-wave features to estimate PGA is a challenging task. One of the reasons for that is that a seismic wave commonly undergoes modification due to various site effects, consequently leading to uncertainty in the predicted PGA. In order to accommodate site effects using site parameters together with P-wave parameters, this paper takes advantage of machine learning to consider multiple parameters simultaneously. Several artificial neural network (ANN) models considering different site effect parameters are constructed. The performances of these ANN models were investigated and compared. In total, 53531 ground motion data obtained from the Taiwan Strong Motion Instrumentation Program were utilized to develop the proposed approach. It was found that the proposed ANN model with horizontal-to-vertical spectral ratio parameters effectively reduces the error of the estimated PGA when compared with either the ANN model without site parameters or the ANN model with other site parameters. [ABSTRACT FROM AUTHOR]

Peak ground acceleration (PGA) can be used to estimate the seismic intensity. However, using P-wave features to estimate PGA is a challenging task. One of the reasons for that is that a seismic wave commonly undergoes modification due to various site effects, consequently leading to uncertainty in the predicted PGA. In order to accommodate site effects using site parameters together with P-wave parameters, this paper takes advantage of machine learning to consider multiple parameters simultaneously. Several artificial neural network (ANN) models considering different site effect parameters are constructed. The performances of these ANN models were investigated and compared. In total, 53531 ground motion data obtained from the Taiwan Strong Motion Instrumentation Program were utilized to develop the proposed approach. It was found that the proposed ANN model with horizontal-to-vertical spectral ratio parameters effectively reduces the error of the estimated PGA when compared with either the ANN model without site parameters or the ANN model with other site parameters. [ABSTRACT FROM AUTHOR]

The strong ground motion of 6 February 2018 M 6.2 Hualien Earthquake triggered a series of co-seismic geomagnetic fluctuations and seismo-traveling atmospheric disturbances (STADs) signatures in infrasonic waves and micro-pressures upon the seismic wave arrival. Networks of 9 QuakeFinder systems, 3 infrasound systems, 2 tiltmeters, 2 micro-barometers, and 11 co-located seismometers are used in this study. Each QuakeFinder system consists of a 3-axes induction magnetometer, an air conductivity sensor, a geophone, and temperature/relative humidity sensors. Co-seismic signatures clearly appear in the induction magnetometers, infrasound systems, and micro-barometers data. The magnetometers register both high- and lowfrequency pulsations. Geomagnetic fluctuations occur upon the seismic wave arrival but last a longer duration, while the STADs lag their co-located seismic waves by about 15 - 45 s. The long-lasting fluctuations recorded by both induction and fluxgate magnetometers suggest that the ground/underground water motion play an important role, which is further conformed by low-frequency fluctuations in the tiltmeter data. In general, the amplitude of geomagnetic fluctuations decays as away from the epicenter. However, unusual large co-seismic geomagnetic fluctuations are detected over areas of the abnormal seismic intensity level and/or the magnetic underground structure with anomalously high susceptibilities. [ABSTRACT FROM AUTHOR]

This study used the results from 45 microtremor array measurements to construct a shallow shear wave velocity structure in the western plain of Taiwan. We constructed a complete 3D velocity model based on shallow and tomography models for our numerical simulation. There are three major subsurfaces, engineering bedrock (VS = 600 m s-1), Pliocene formation and Miocene formation, constituted in the shallow model. The constant velocity is given in each subsurface. We employed a 3D-FD (finite-differences) method to simulate seismic wave propagation in the western plain. The aim of this study was to perform a quantitative comparison of site amplifications and durations obtained from empirical data and numerical modelling in order to obtain the shallow substructure soil response. Modelling clearly revealed that the shallow substructure plays an important role in strong ground motion prediction using 3D simulation. The results show significant improvements in effective shaking duration and the peak ground velocity (PGV) distribution in terms of the accuracy achieved by our developed model. We recommend a high-resolution shallow substructure as an essential component in future seismic hazard analyses. [ABSTRACT FROM AUTHOR]