Ground motion prediction at gravitational wave observatories using archival seismic data
Abstract
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.
Cite
@article{arxiv.1812.05185,
title = {Ground motion prediction at gravitational wave observatories using archival seismic data},
author = {Nikhil Mukund and Michael Coughlin and Jan Harms and Sebastien Biscans and Jim Warner and Arnaud Pele and Keith Thorne and David Barker and Nicolas Arnaud and Fred Donovan and Irene Fiori and Hunter Gabbard and Brian Lantz and Richard Mittleman and Hugh Radkins and Bas Swinkels},
journal= {arXiv preprint arXiv:1812.05185},
year = {2019}
}
Comments
10 pages, 7 figures; matches published version