Limiting the effects of earthquakes on gravitational-wave interferometers

TL;DR

This paper presents an early warning system leveraging real-time earthquake alerts and machine learning to predict and mitigate the impact of seismic events on gravitational-wave detectors, enhancing their operational stability.

Contribution

It introduces a novel earthquake early warning and prediction system specifically designed to protect gravitational-wave interferometers from seismic disruptions.

Findings

90% of ground-motion predictions are within a factor of 5 of actual values

The system can prevent detector interruptions for 40-100 earthquakes over six months

Minimal error when using preliminary earthquake data for predictions

Abstract

Ground-based gravitational wave interferometers such as the Laser Interferometer Gravitational-wave Observatory (LIGO) are susceptible to high-magnitude teleseismic events, which can interrupt their operation in science mode and significantly reduce the duty cycle. It can take several hours for a detector to stabilize enough to return to its nominal state for scientific observations. The down time can be reduced if advance warning of impending shaking is received and the impact is suppressed in the isolation system with the goal of maintaining stable operation even at the expense of increased instrumental noise. Here we describe an early warning system for modern gravitational-wave observatories. The system relies on near real-time earthquake alerts provided by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). Hypocenter and magnitude information is generally available in 5 to 20 minutes of a significant earthquake depending on its magnitude and location. The alerts are used to estimate arrival times and ground velocities at the gravitational-wave detectors. In general, 90\% of the predictions for ground-motion amplitude are within a factor of 5 of measured values. The error in both arrival time and ground-motion prediction introduced by using preliminary, rather than final, hypocenter and magnitude information is minimal. By using a machine learning algorithm, we develop a prediction model that calculates the probability that a given earthquake will prevent a detector from taking data. Our initial results indicate that by using detector control configuration changes, we could prevent interruption of operation from 40-100 earthquake events in a 6-month time-period.