Improving earthquake monitoring for gravitational-wave detectors with historical seismic data

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Abstract:: A remarkable level of isolation from the ground is required for Advanced gravitational-wave detectors such as the Laser Interferometer Gravitational-Wave Observatory LIGO) to function at peak performance. These ground based detectors are susceptible to high magnitude teleseismic events such as earthquakes, which can disrupt proper functioning, operation and significantly reduce their duty cycle. As a result, data is lost and it can take several hours for a detector to stabilize and return to the proper state for scientific observations. With advanced warning of impeding tremors, the impact can be suppressed in the isolation system and the down time can be reduced at the expense of increased instrumental noise. An earthquake early- warning system has been developed relying on near real-time earthquake alerts provided by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). The alerts can be used to estimate arrival times and ground velocities at the gravitational- wave detectors. By using machine learning algorithms, a prediction model and control strategy is developed to reduce LIGO downtime by ~30\%. This paper presents improvements under consideration to better develop that prediction model and decrease interruptions during LIGO operation.

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LIGO Document P1800130-v2