Search

Proposed in the late 1980s, cumulative absolute velocity (CAV) is a new intensity measure for earthquake ground motion characterizations, followed by studies and applications such as CAV ground motion prediction equations (GMPEs). In this study, two new CAV GMPEs were developed with 24,667 strong-motion records from Taiwan, and the first CAV seismic hazard assessment for Taipei (the most important city in Taiwan) was then conducted using the local CAV models. It shows that the annual rate for the study area to encounter a ground motion with CAV >0.97 g-sec is 0.002 per year, corresponding to a 10% occurrence probability in 50 years. By contrast, the deterministic scenario-based analysis shows that the CAV seismic hazard is about 0.60 g-sec for the study area. Future studies are worth conducting to develop more sophisticated, local CAV GPMEs and to explore more applications of such CAV prediction models, such as the developments of PGA-CAV joint probability distributions for conducting PGA-CAV joint seismic hazard assessments. • First CAV GMPE for Taiwan areas. • More than 4,000 strong motion records used. • CAV-based seismic hazard analysis for Taipei. [ABSTRACT FROM AUTHOR]

Many earthquake empirical models were developed based on the statistics in the past. However, it is commonly seen that a non-local model was applied to a local study without any adjustment. In this paper, a new algorithm using maximum likelihood estimation (MLE) to adjust a non-local model for local applications was presented, including a case study assessing the probability of major earthquake occurrences in Taipei. Specifically, considering the fault length of 36 km and slip rate of 2 mm/yr, it suggests the Sanchiao (or Shanchiao) fault could induce a major earthquake with magnitude M w 7.14±0.17, based on a non-local model integrated with limited local data using the MLE algorithms. [ABSTRACT FROM AUTHOR]

Earthquake early warning (EEW) has been implemented in several regions around the world. However, because of natural randomness and uncertainty, false alarm and missed alarm can be expected in EEW. The key scope of this study is to evaluate the reliability of an on-site EEW in Taiwan, by testing the system's algorithm with 17,836 earthquake data from 1999 to 2013. The analysis shows that the on-site EEW system, empirically speaking, should have a false-alarm probability of 2.5%, and a missed-alarm probability of 14.1%. Considering missed alarm should be more critical to EEW, a new algorithm that could reduce the system's missed-alarm occurrences to 6% is also discussed in this paper. [ABSTRACT FROM AUTHOR]

This study aims to develop a joint probability function of peak ground acceleration (PGA) and cumulative absolute velocity (CAV) for the strong ground motion data from Taiwan. First, a total of 40,385 earthquake time histories are collected from the Taiwan Strong Motion Instrumentation Program. Then, the copula approach is introduced and applied to model the joint probability distribution of PGA and CAV. Finally, the correlation results using the PGA-CAV empirical data and the normalized residuals are compared. The results indicate that there exists a strong positive correlation between PGA and CAV. For both the PGA and CAV empirical data and the normalized residuals, the multivariate lognormal distribution composed of two lognormal marginal distributions and the Gaussian copula provides adequate characterization of the PGA-CAV joint distribution observed in Taiwan. This finding demonstrates the validity of the conventional two-step approach for developing empirical ground motion prediction equations (GMPEs) of multiple ground motion parameters from the copula viewpoint. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study first presents the series of peak ground acceleration (PGA) in the three major cities in Taiwan. The PGAs are back-calculated from an earthquake catalog with the use of ground motion models. The maximums of the 84th percentile (mean + one standard deviation) PGA since 1900 are 1.03, 0.36, and 0.10 g, in Taipei, Taichung, and Kaohsiung, respectively. Statistical goodness-of-fit testing shows that the series of PGA follow a double-lognormal distribution. Using the verified probability distribution, a probabilistic analysis was developed in this paper, and used to evaluate probability-based seismic hazard. Accordingly, given a PGA equal to 0.5 g, the annual exceedance probabilities are 0.56, 0.46, and 0.23 % in Taipei, Taichung, and Kaohsiung, respectively; for PGA equal to 1.0 g, the probabilities become 0.18, 0.14, and 0.09 %. As a result, this analysis indicates the city in South Taiwan is associated with relatively lower seismic hazard, compared with those in Central and North Taiwan. [ABSTRACT FROM AUTHOR]

It is understood that sample size could be an issue in earthquake statistical studies, causing the best estimate being too deterministic or less representative derived from limited statistics from observation. Like many Bayesian analyses and estimates, this study shows another novel application of the Bayesian approach to earthquake engineering, using prior data to help compensate the limited observation for the target problem to estimate the magnitude of the recurring Meishan earthquake in central Taiwan. With the Bayesian algorithms developed, the Bayesian analysis suggests that the next major event induced by the Meishan fault in central Taiwan should be in M w 6.44±0.33, based on one magnitude observation of M w 6.4 from the last event, along with the prior data including fault length of 14 km, rupture width of 15 km, rupture area of 216 km 2 , average displacement of 0.7 m, slip rate of 6 mm/yr, and five earthquake empirical models. [ABSTRACT FROM AUTHOR]