Ground motion prediction at gravitational wave observatories using archival seismic data
Published in Classical and Quantum Gravity, 2019
Recommended citation: Mukund, Nikhil, Michael Coughlin, Jan Harms, Sebastien Biscans, Jim Warner, Arnaud Pele, Keith Thorne, et al. “Ground Motion Prediction at Gravitational Wave Observatories Using Archival Seismic Data.” Classical and Quantum Gravity 36, no. 8 (January 2019): 085005. https://doi.org/10.1088/1361-6382/ab0d2c. https://iopscience.iop.org/article/10.1088/1361-6382/ab0d2c/meta
Gravitational wave observatories have always been affected by tele-seismic earthquakes leading to a decrease in duty cycle and coincident observation time. In this analysis, we leverage the power of machine learning algorithms and archival seismic data to predict the ground motion and the state of the gravitational wave interferometer during the event of an earthquake. We demonstrate improvement from a factor of 5 to a factor of 2.5 in scatter of the error in the predicted ground velocity over a previous model fitting based approach. The level of accuracy achieved with this scheme makes it possible to switch control configuration during periods of excessive ground motion thus preventing the interferometer from losing lock. To further assess the accuracy and utility of our approach, we use IRIS seismic network data and obtain similar levels of agreement between the estimates and the measured amplitudes. The performance indicates that such an archival or prediction scheme can be extended beyond the realm of gravitational wave detector sites for hazard-based early warning alerts.
Recommended citation: ‘Mukund, Nikhil, Michael Coughlin, Jan Harms, Sebastien Biscans, Jim Warner, Arnaud Pele, Keith Thorne, et al. “Ground Motion Prediction at Gravitational Wave Observatories Using Archival Seismic Data.” Classical and Quantum Gravity 36, no. 8 (January 2019): 085005. https://doi.org/10.1088/1361-6382/ab0d2c.’