Landslides induced by typhoon Morakot during its passage across Taiwan on 7-9 Aug 2009 claimed more than 700 lives and caused heavy economic loss. Unlike earthquake monitoring, precise locations of landslides could not be determined in nearreal time because their seismic phases are difficult to identify. Here, we show that large, damaging landslide events are characterized seismically by a distinct waveform pattern of frequent intermixes of P and S waves over a time window of several tens of seconds. The predominant frequency band during these time windows ranges from 0.5 to 5Hz. The high-frequency content is clearly deficient relative to that of local earthquakes by about one to two orders. We also demonstrate that large landslide events can be located and monitored with algorithms specifically designed for real-time seismic applications. This near-real-time monitoring capability would be particularly useful for emergency responders and government organizations to coordinate effective relief-andrescue operations.

M ≥ 3 earthquakes which occurred in the Taipei Metropolitan Area from 1973 through 2010 are used to study seismicity of the area. First, the epicentral distribution, depth distribution, and temporal sequences of earthquake magnitudes are described. The earthquakes can be divided into two groups: one for shallow events with focal depths ranging 0 -40 km and the other with focal depths deeper than 60 km. Shallow earthquakes are mainly located in the depth range from 0 -10 km north of 25.1°N, and down to 35 km for those south of 25.1°N. Deep events are located in the subduction zone, with a dip angle of about 70°. Three statistical models, the gamma, power-law, and exponential functions, are applied to describe the single frequency distribution of inter-occurrence times between two consecutive events for both shallow and deep earthquakes. Numerical tests suggest that the most appropriate time interval for counting the frequency of events for statistical analysis is 10 days. Results show that among the three functions, the power-law function is the most appropriate for describing the data points. While the exponential function is the least appropriate to describe the observations, thus, the time series of earthquakes in consideration are not Poissonian. The gamma function is less and more appropriate to describe the observations than the power-law function and the exponential function, respectively. The scaling exponent of the power-law function decreases linearly with an increasingly lower-bound magnitude. The slope value of the regression equation is smaller for shallow earthquakes than for deep events. Meanwhile, the power-law function cannot work when the lower-bound magnitude is 4.2 for shallow earthquakes and 4.3 for deep events.

The generalized fractal dimensions are measured for M ≥ 3 shallow earthquakes with focal depths of ≤ 40 km in the Taipei Metropolitan Area (from 121.3 to 121.9°E and 24.8 to 25.3°N) over the 1973 -2010 period based on spatial distribution (using epicentral and hypocentral distances between two events, r) and time sequence (using the inter-event time between two events, t). The multifractal measures are estimated from log-log plots of C q (r) versus r and C q (t) versus t, where C q (r) and C q (t) are the generalized correlation integral, respectively, of r and t at positive q. For the spatial distribution, C q (r) is calculated based on the epicentral distance (i.e., the 2D measure) and hypocentral distance (i.e., the 3D measure). Under both 2D and 3D measures, the log-log plot of C q (r) versus r shows a linear distribution when log(r o ) ≤ log(r) ≤ log(r ub ) and roll-over when r > r ub . For all cases log(r o ) is 0.3, and log(r ub ) are 1.7 and 1.4 for the 2D and 3D measures, respectively. D q , which is the slope of the linear portion, monotonically decreases with increasing q, thus indicating that the epicentral and hypocentral distributions of earthquakes are multifractal. The values of D q are lower than 2 and 3, respectively, for the 2D and 3D measures. For the time sequence of the events in study, C q (t) is calculated based on the inter-event time between two events. The log-log plot of C q (t) versus t does not seem able to show a linear relationship in a large range of log(r) or r and the value of D q cannot be evaluated, thus suggesting that the time sequence of M ≥ 3 shallow earthquakes in the Taipei Metropolitan Area (TMA) is not multifractal.

High seismicity with spatial heterogeneity in Taiwan makes this region one of the best natural laboratories for seismological researches. Numerous seismicity studies, including the b-value, have been performed for more than one century. One of the possible ways to mitigate seismic risk is predicting an impending earthquake through various kinds of seismic precursors observations. The first seismic precursors project in Taiwan started in 1978. The temporal variation in b-value prior to a forthcoming earthquake has been considered a significant seismic precursor for earthquake prediction. In this review study, we focus on the studies of b-values of earthquakes in Taiwan, including the spatial distribution of b-values, temporal variation in b-values, correlation between the b-value and fractal dimension, and correlation between the band p-value of Omori law for aftershocks. Also included is the relation between the spatial distribution of b-values and regional geotectonics. The properties and controlling factors of b-value and earthquake monitoring in Taiwan will also be briefly described.

M L ≥ 3 earthquakes (M L = local magnitude) that occurred in the Taipei Metropolitan Area (TMA) from 1973-2013 are selected to study the dominant seismicity period of this area. The epicentral distribution and temporal sequences of earthquake magnitudes are simply described. These earthquakes can be divided into two groups: one for events shallower than 40 km and one for events deeper than 60 km. Shallow earthquakes are located mainly in the 0-10 km depth range north of 25.1°N, and down to 35 km for those south of 25.1°N. Deep events are located in the subduction zone, with a dip angle of about 70°. The Morlet wavelet technique is applied to analyze the dominant periods of temporal variations in numbers of monthly earthquakes in the shallow and deep ranges for three magnitude ranges, i.e., M L ≥ 3, 4, and 5. The results show that for shallow earthquakes the dominant periods are 15.4, 30.8, 66.1, and 132.2 months when M L ≥ 3 and 30.8 months when M L ≥ 4; while for deep earthquakes, the dominant periods are 16.5 and 141.7 months when M L ≥ 3 and 141.7 months when M L ≥ 4. The dominant period cannot be obtained for both shallow and deep M L ≥ 5 earthquakes.