Ground motion prediction at gravitational wave observatories using archival seismic data

Nikhil Mukund, Michael Coughlin, Jan Harms, Sebastien Biscans, Jim Warner, Arnaud Pele, Keith Thorne, David Barker, Nicolas Arnaud, Fred Donovan, Irene Fiori, Hunter Gabbard, Brian Lantz, Richard Mittleman, Hugh Radkins, Bas Swinkels

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Article number085005
JournalClassical and Quantum Gravity
Volume36
Issue number8
DOIs
StatePublished - Apr 1 2019
Externally publishedYes

Keywords

  • GW detectors
  • earthquake early warning
  • machine learning
  • seismic Rayleigh waves

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