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We have developed a real-time moment tensor monitoring system (RMT) which takes advantage of a grid-based moment tensor inversion technique and real-time broad-band seismic recordings to automatically monitor earthquake activities in the vicinity of Taiwan. The centroid moment tensor (CMT) inversion technique and a grid search scheme are applied to obtain the information of earthquake source parameters, including the event origin time, hypocentral location, moment magnitude and focal mechanism. All of these source parameters can be determined simultaneously within 117 s after the occurrence of an earthquake. The monitoring area involves the entire Taiwan Island and the offshore region, which covers the area of 119.3°E to 123.0°E and 21.0°N to 26.0°N, with a depth from 6 to 136 km. A 3-D grid system is implemented in the monitoring area with a uniform horizontal interval of 0.1° and a vertical interval of 10 km. The inversion procedure is based on a 1-D Green's function database calculated by the frequency–wavenumber (fk) method. We compare our results with the Central Weather Bureau (CWB) catalogue data for earthquakes occurred between 2010 and 2012. The average differences between event origin time and hypocentral location are less than 2 s and 10 km, respectively. The focal mechanisms determined by RMT are also comparable with the Broadband Array in Taiwan for Seismology (BATS) CMT solutions. These results indicate that the RMT system is realizable and efficient to monitor local seismic activities. In addition, the time needed to obtain all the point source parameters is reduced substantially compared to routine earthquake reports. By connecting RMT with a real-time online earthquake simulation (ROS) system, all the source parameters will be forwarded to the ROS to make the real-time earthquake simulation feasible. The RMT has operated offline (2010–2011) and online (since January 2012 to present) at the Institute of Earth Sciences (IES), Academia Sinica (http://rmt.earth.sinica.edu.tw). The long-term goal of this system is to provide real-time source information for rapid seismic hazard assessment during large earthquakes. [ABSTRACT FROM AUTHOR]

The rapid detection and characterization of megathrust earthquakes is a difficult task given their large rupture zone and duration. These events produce very strong ground vibrations in the near field that can cause weak motion instruments to clip, and they are also capable of generating large-scale tsunamis. The 2011 M 9 Tohoku-oki earthquake that occurred offshore Japan is one member of a series of great earthquakes for which extended geophysical observations are available. Here, we test an automated scanning algorithm for great earthquakes using continuous very long-period (100–200 s) seismic records from K-NET strong-motion seismograms of the earthquake. By continuously performing the cross-correlation of data and Green's functions (GFs) in a moment tensor analysis, we show that the algorithm automatically detects, locates and determines source parameters including the moment magnitude and mechanism of the great Tohoku-oki earthquake within 8 min of its origin time. The method does not saturate. We also show that quasi-finite-source GFs, which take into account the effects of a finite-source, in a single-point source moment tensor algorithm better fit the data, especially in the near-field. We show that this technique allows the correct characterization of the earthquake using a limited number of stations. This can yield information usable for tsunami early warning. [ABSTRACT FROM AUTHOR]