Microseismic Noise Mitigation with Machine Learning for Advanced LIGO

TL;DR

This paper presents a machine learning approach to model and suppress residual microseismic noise in LIGO, significantly improving seismic isolation and potentially enhancing gravitational wave detection sensitivity.

Contribution

The study introduces a neural network-based method for predicting and reducing residual seismic motion, outperforming traditional linear filtering techniques in LIGO's seismic isolation system.

Findings

Up to tenfold reduction in residual platform motion.

Nonlinear couplings significantly affect isolation performance.

Machine learning enhances low-frequency stability of detectors.

Abstract

The unprecedented sensitivity of the Laser Interferometer Gravitational-Wave Observatory, which enables the detection of distant astrophysical sources, also renders the detectors highly susceptible to low-frequency ground motion. Persistent microseisms in the 0.1-0.3 Hz band couple into the instruments, degrade lock stability, and contribute substantially to detector downtime during observing runs. The multi-stage seismic isolation system has achieved remarkable success in mitigating such disturbances through active feedback control, yet residual platform motion remains a key factor limiting low-frequency sensitivity and duty cycle. Further reduction of this residual motion is therefore critical for improving the long-term stability and overall astrophysical reach of the observatories. In this work, we develop a data-driven approach that uses machine learning to model and suppress residual seismic motion within the isolation system. Ground and platform sensor data from the detectors are used to train a neural network that predicts platform motion driven by microseismic activity. When incorporated into the control scheme, the network's predictions yield up to an order-of-magnitude reduction in residual motion compared to conventional linear filtering methods, revealing that nonlinear couplings play a significant role in limiting current isolation performance. These results demonstrate that machine-learning-based control can provide a powerful new pathway for enhancing active seismic isolation, improving lock robustness, and extending the low-frequency observational capabilities of gravitational-wave detectors.