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The 2018 Mw 6.4 Hualien earthquake generated a large peak-to-peak velocity of over 2 m/s, with a period of 3 s at the south end of the Milun fault, which resulted in the collapse of five buildings. To investigate the shallow subsurface soil structure and evaluate possible effects on the ground motion and building damage, we performed microtremor measurements in the Hualien basin. Based on the velocity structure jointly inverted from both Rayleigh-wave dispersion curves and microtremor horizontal-to-vertical spectral ratio data, we found that the shallow subsurface structure generally deepens from west to east. Close to the Milun fault, the structure becomes shallower, which is consistent with faulting during the 2018 earthquake and the long-term tectonic displacement. There is no significant variation for the site conditions in the north–south direction that can explain the large peak ground velocity in the south. As a result of the dense measurements in the heavily damaged area, where three high-rise buildings totally collapsed, these locations have the average S-wave velocity of the upper 30 m (AVS30) values and are relatively high compared to the more distant area from the Meilun River. This is somewhat unusual, because lower AVS30 values indicating softer ground conditions are expected close to the river. We did not find any characteristic subsurface soil structure that may contribute to the building collapses. The large 3 s pulse was probably generated by source effects, rather than subsurface soil amplification.

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.

Broadband seismometers, ground-based Global Navigation Satellite Systems (GNSS), and magnetometers that were located within an epicentral distance of approximately 150 km consistently observed the novel anomalous behaviors of the common-mode ground vibrations approximately 5–10 days before the M6.6 Meinong earthquake in Taiwan. The common-mode ground vibrations with amplitudes near 0.1 m at frequencies ranging from 8 × 10−5 to 2 × 10−4 Hz were generated near the region close to the epicenter of the impending earthquake. The common-mode vibrations were consistently observed in seismic and GNSS data associated with five other earthquakes in four distinct areas. The results reveal that the common-mode vibrations could be a typical behavior before earthquakes. The causal mechanism of common-mode vibrations can be attributed to crustal resonance excitations before fault dislocations occur. Potential relationships with other pre-earthquake anomalies suggest that the common-mode vibrations could be ground motion before earthquakes, which was investigated for a significant length of time.

On February 12, 2014, an ML 4.2 earthquake (named 0212 EQ) occurred at the Tatun Volcano Group (TVG) in northern Taiwan. Seismic data from both the regional broadband array in Taiwan and a local dense seismic network in the TVG indicate that the 0212 EQ was located at a depth of 4.3 km near Shamoshan, one of the latest eruptive units in the TVG. Referring to the aftershocks surrounding the 0212 EQ, we further found that (1) they formed a linear seismic zone dipping ∼50° towards the southeast to reach the Shanchiao Fault that cuts through the TVG, (2) focal mechanism of more than half of the larger aftershocks (MD ≥ 1.5) matched this linear seismic zone for a normal faulting, and (3) the seismic b-value was calculated to be 3.55. They are used to delineate the geometry of the Shanchiao Fault at the upper crust and to infer that localized geothermal fluid might have affected the activity of aftershocks as manifested by the very high seismic b-value. The strongly compensated linear vector dipole of the centroid moment tensor suggests the 0212 EQ had a non-double-couple source resulted from the gravitational collapse. Thus, we conclude that the 0212 EQ was a combined synergistic effect of a collapsed reservoir filled with volcanic fluids and faulting activities along the Shanchiao Fault.

Long-period pulse-like strong ground motions were recorded near the Milun fault and caused serious building damages in the Hualien City during the main shock of the 2018 Hualien, Taiwan, earthquake. In this paper, questionnaire survey on feeling of shaking, indoor damages, structural, and nonstructural damages to high-rise reinforced concrete residential buildings were analyzed by three building groups according to the positions to the Milun fault and different site conditions. Relationships between the results of questionnaire survey on building damages and characteristics of strong ground motions were studied. Furthermore, a characterized seismic source model was proposed to elucidate the generation mechanism of near-fault long-period strong ground motions. In this model, a shallow strong motion generation area (SMGA7) in the south part of Milun fault with rise-time of 2.6 s contributed to the long-period pulse-like strong ground motions. Besides, a deep SMGA (SMGA8) under the SMGA7 with rise-time of 1.2 s also contributed to the strong ground motions. Using this model, strong ground motions at the sites of buildings for questionnaire survey were synthesized. Because of the directivity effects, amplitudes of ground motions were increased from north to south along the Milun fault, which can explain the serious building damages at the buildings located at the southwest to the Milun fault. The reasons of building damages were also examined from the aspect of predominant periods of ground sites and fundamental periods of buildings, which were extracted from microtremor measurement. Damages at the buildings located on the central part of the hanging wall with relatively good site condition were the lightest for almost all the surveyed items. Pulses with period in the range of 1 s–2 s which were generated by the SMGA8 and amplified by the near surface layers are considered to be responsible for building damages.

In this study, an empirical transfer function (ETF), which is the spectrum difference in Fourier amplitude spectra between observed strong ground motion and synthetic motion obtained by a stochastic point-source simulation technique, is constructed for the Taipei Basin, Taiwan. The basis stochastic point-source simulations can be treated as reference rock site conditions in order to consider site effects. The parameters of the stochastic point-source approach related to source and path effects are collected from previous well-verified studies. A database of shallow, small-magnitude earthquakes is selected to construct the ETFs so that the point-source approach for synthetic motions might be more widely applicable. The high-frequency synthetic motion obtained from the ETF procedure is site-corrected in the strong site-response area of the Taipei Basin. The site-response characteristics of the ETF show similar responses as in previous studies, which indicates that the base synthetic model is suitable for the reference rock conditions in the Taipei Basin. The dominant frequency contour corresponds to the shape of the bottom of the geological basement (the top of the Tertiary period), which is the Sungshan formation. Two clear high-amplification areas are identified in the deepest region of the Sungshan formation, as shown by an amplification contour of 0.5 Hz. Meanwhile, a high-amplification area was shifted to the basin’s edge, as shown by an amplification contour of 2.0 Hz. Three target earthquakes with different kinds of source conditions, including shallow small-magnitude events, shallow and relatively large-magnitude events, and deep small-magnitude events relative to the ETF database, are tested to verify site correction. The results indicate that ETF-based site correction is effective for shallow earthquakes, even those with higher magnitudes, but is not suitable for deep earthquakes. Finally, one of the most significant shallow large-magnitude earthquakes (the 1999 Chi-Chi earthquake in Taiwan) is verified in this study. A finite fault stochastic simulation technique is applied, owing to the complexity of the fault rupture process for the Chi-Chi earthquake, and the ETF-based site-correction function is multiplied to obtain a precise simulation of high-frequency (up to 10 Hz) strong motions. The high-frequency prediction has good agreement in both time and frequency domain in this study, and the prediction level is the same as that predicted by the site-corrected ground motion prediction equation.

In this study, microtremor array measurements were conducted at 45 sites in the Western Plain of Taiwan. The arrays were approximately 30 m or 60 m in radius, depending on the site. The maximum-likelihood frequency–wavenumber method was adopted to obtain the phase velocities of Rayleigh waves, and then a genetic algorithm technique based on an inversion scheme of the fundamental mode of the Rayleigh waves’ dispersion curves was applied to calculate a preliminary S-wave velocity (Vs) profile at each site. Because a layer of thick sediment covers the bedrock in the Western Plain of Taiwan, microtremor arrays in this size range cannot estimate the structure of the entire sediment. Therefore, this study implemented further inversion of the horizontal-to-vertical spectral ratios of the microtremors to estimate the deeper structures up to the bedrock of Vs greater than 1000 m/s. Previously logged velocity profiles for different depths at or near our study sites were collected and compared with the Vs profiles derived from our microtremor array measurements; the results were found to be highly comparable. Therefore, we could delineate the depth distributions for the layer depths for Vs = 600 m/s and 1000 m/s in this region. The depth for Vs = 600 m/s is approximately 50 m in the piedmont area and approximately 300 m at the coastline; moreover, the depths for Vs = 1000 m/s increase from 200 m in the piedmont area to approximately 1000 m at the coastline. The depths for Vs = 1.0 km/s (Z1.0), which is an important parameter that accounts for the basin effect in recent ground motion prediction equations, are consequently available at the study sites. The distribution of Z1.0 as a function of Vs30 indicates higher similarity to that in Japan than in the San Francisco Bay area.

The National Center for Research on Earthquake Engineering in Taiwan has developed an on-site earthquake early warning system (NEEWS). The Meinong earthquake with a moment magnitude of 6.53 and a focal depth of 14.6 km occurred on 5 February 2016 in southern Taiwan. It caused 117 deaths, injured 551, caused the collapse of six buildings, and serious damage to 247 buildings. During the Meinong earthquake, the system performance of 16 NEEWS stations was recorded. Based on a preassigned peak ground acceleration (PGA) threshold to issue alarms at different stations, no false alarms or missed alarms were issued during the earthquake. About 4 s to 33 s of lead time were provided by the NEEWS depending on the epicenter distance. In addition, the directivity of the earthquake source characteristic and also possibly the site effects were observed in the diagram of the distribution of PGA difference between the predicted PGA and the measured PGA.

On 5 February 2016, a moderate earthquake occurred in southwestern Taiwan with M L 6.4 and a focal depth of 16.7 km. This earthquake caused damage to a few buildings and 117 casualties. A low‐cost earthquake early warning (EEW) system ( P ‐alert) is in operation for the purpose of EEW and for providing near‐real‐time shake maps. During this event, a detailed shaking map was generated by the P ‐alert system within 2 min after the earthquake occurrence, and the high shaking regions strongly correlated with the locations in which the damage and casualties occurred. In the field, individual P ‐alert devices also serve as onsite EEW systems using P ‐wave information. The individual P ‐alert provided a 4–8 s lead time before the arrival of violent shaking in the damaged regions. For regional EEW, both the Central Weather Bureau (CWB, official agency) and the P ‐alert system responded very well. Currently, regional warnings in Taiwan are only provided to cities at epicentral distances of 50 km or more by the CWB. For cities within a 50‐km epicentral distance, the P ‐alert system could be useful for providing onsite EEW. The performance of the P ‐alert network during this earthquake proves the efficiency of this real‐time, low‐cost network in terms of early warning (regional and onsite), near‐real‐time shake maps, rapid reports, and strong‐motion data for research purposes.

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 (V_S = 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.

It is seen that strong motion generation area plays an important role in the shaping of strong motion records at the observation point. Strong motion generation areas identified within the rupture plane of the 27 March 2013 Nantou, Taiwan, earthquake (Mw = 5.9) have been modelled in this work. It is seen that all available records are at the surface which include site amplification terms. The modified semi-empirical technique effectively simulates records at rock site. The site amplification terms in all records have been removed using SHAKE 91 program and velocity input at each site. The observed records corrected for site amplification terms are further used for comparison with simulated record at the bedrock from several models. Once the observed records at soil sites are transferred at the bedrock, the next task is selection of final model that gives best fit records. Peak ground acceleration from simulated record at four sites is compared with that from corrected observed data. Since the semi-empirical technique of simulation is strongly dependent on various modelling parameters such as dip, strike, rake, rupture velocity and starting points of rupture, these parameters change iteratively in a specified range in a heuristic way to obtain best modelling parameters. The model giving minimum root mean square error (RMSE) is retained at final model. It is seen that minimum root mean square error of the wave form comparison has been obtained at four stations for the source model having single strong motion generation area. Strong motion records have been simulated at four different recording stations. Comparison of observed and simulated records has been made in terms of RMSE between simulated and observed acceleration records, velocity records and the response spectra at each of four stations. Comparison of waveforms and parameters extracted from observed and simulated records confirms the efficacy of the modified technique to model earthquake characterized by SMGAs.

This study extracted some P-wave features from the first few seconds of vertical ground acceleration of a single station. These features include the predominant period, peak acceleration amplitude, peak velocity amplitude, peak displacement amplitude, cumulative absolute velocity and integral of the squared velocity. The support vector regression was employed to establish a regression model which can predict the peak ground acceleration according to these features. Some representative earthquake records of the Taiwan Strong Motion Instrumentation Program from 1992 to 2006 were used to train and validate the support vector regression model. Then the constructed model was tested using the whole earthquake records of the same period as well as the 2010 Kaohsiung earthquake with 6.4 M L . The effects on the performance of the regression models using different P-wave features and different length of time window to extract these features are studied. The results illustrated that, if the first 3 s of the vertical ground acceleration was used, the standard deviation of the predicted peak ground acceleration error of the whole tested 15-years earthquake records is 20.89 gal.The length of time window could be shortened, e.g. 1 s, and the prediction error is slightly sacrificed, in order to prolong the lead-time before destructive S-waves reaches.

The shear wave velocity of shallow sediments is very important in seismic wave amplification and thus Vs 30 is a well-known parameter for site classification. Based on the relationship between Vs and soil indexes, the empirical regression equations were evaluated using more than 600 data sets from the Ilan area and the Taipei Basin. Multivariable analysis, which can increase the accuracy of regression equations, was used in this study. Three extrapolations were compared, and then the most accurate bottom-constant extrapolation was adopted to estimate Vs 30 at 16 boreholes, none of which reached a depth of 30 m. Ultimately Vs 30 was derived for 110 free-field strong motion stations, and the stations in northeastern Taiwan were reclassified according to the Vs 30-based NEHRP provisions. Regression equations of Vs and the extrapolation of Vs 30 were also applied to boreholes with only N -values and corresponding depths of less than 30 m for assessing the Vs 30 and NEHRP class.

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 microearthquake 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¢X 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.

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.