Three algorithms for rapidly detecting the start of an earthquake have been developed and will be improved and integrated into the working earthquake early warning system:
ElarmS – ElarmS, or Earthquake Alarm Systems, detects the initial energy to radiate from an earthquake, the P-wave energy, which rarely causes and damage. Using the arrival time of the P-wave at the surface and the frequency content measured at several stations, ElarmS estimates the location and the magnitude of the earthquake. The anticipated ground shaking across the affected region is then estimated using empirical attenuation relations. The methodology can provide warning before the S-wave arrival which usually causes most of the damage. Learn More about the Elarms Algorithm
Onsite – The τc-Pd Onsite algorithm is a single-sensor approach to EEW (Kanamori, 2005). The τc-Pd Onsite algorithm uses the period τc and amplitude Pd of initial shaking to estimate the size and forthcoming shaking in an earthquake. In principle, this type of warning approach can more quickly detect earthquakes, but is expected to be less reliable compared to regional warning algorithms that are based on observations at multiple seismic sensors. Learn More about the On-Site Algorithm
Virtual Seismologist – The Virtual Seismologist (VS method) uses envelopes of acceleration, velocity, and displacement as the basic data input to a Bayesian framework that also incorporates other types of information (e.g., topology of the seismic network, recent seismic activity). The system is named the Virtual Seismologist (VS method) since the goal is to mimic the type of robust analysis that a human would perform, but in a greatly reduced amount of time. Learn More about the Virtual Seismologist algorithm.
These algorithms each evaluate the data and independently produce estimates of an evolving earthquake’s location and magnitude. These results are evaluated by a module that combines the information from the three algorithms to produce a single estimate of the earthquake characteristics with a measure of the probability, or confidence, in the result. This Decision Module then broadcasts the results.