Limiting the effects of earthquakes on gravitational-wave interferometers
Limiting the effects of earthquakes on gravitational-wave interferometers
复制标题
限制地震对引力波干涉仪的影响
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
M. Perry
中科院分区：
文献类型：
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作者：
M. Coughlin;P. Earle;J. Harms;S. Biscans;C. Buchanan;E. Coughlin;F. Donovan;J. Fee;H. Gabbard;M. Guy;N. Mukund;M. Perry
地面引力波干涉仪，如激光干涉仪引力波天文台（LIGO），容易受到高震级地震事件的地面震动，这可能会中断其在科学模式下的操作，并显着降低其占空比。探测器可能需要几个小时才能稳定下来，恢复到正常状态进行科学观测。如果收到即将发生的震动的提前警告，并且在隔离系统中抑制冲击，即使以增加仪器噪声为代价也能保持稳定运行，则可以减少停机时间。在这里，我们描述了一个早期预警系统的现代引力波观测站。该系统依赖于美国地质调查局（USGS）和国家海洋和大气管理局（NOAA）提供的近实时地震警报。根据地震的震级和位置，在发生重大地震后的5到20分钟内，通常可以获得初步的低潜伏期震源和震级信息。警报用于估计引力波探测器的到达时间和地面速度。一般来说，90%的预测地面运动振幅的测量值的5倍之内。在到达时间和地面运动预测引入的误差，而不是最终，震源和震级信息是最小的。通过使用机器学习算法，我们开发了一个预测模型，该模型可以计算给定地震阻止探测器获取数据的概率。我们的初步结果表明，通过使用检测器控制配置的变化，我们可以防止中断的操作从40到100个地震事件在6个月的时间内。
Ground-based gravitational wave interferometers such as the Laser Interferometer Gravitational-wave Observatory (LIGO) are susceptible to ground shaking from high-magnitude teleseismic events, which can interrupt their operation in science mode and significantly reduce their 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). Preliminary low latency hypocenter and magnitude information is generally available in 5 to 20 min 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 to 100 earthquake events in a 6-month time-period.