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Ground motion prediction at gravitational wave observatories using archival seismic data

Mukund, Nikhil and Coughlin, Michael and Harms, Jan and Biscans, Sebastien and Warner, Jim and Pele, Arnaud and Thorne, Keith and Barker, David and Arnaud, Nicolas and Donovan, Fred and Fiori, Irene and Gabbard, Hunter and Lantz, Brian and Mittleman, Richard and Radkins, Hugh and Swinkels, Bas (2019) Ground motion prediction at gravitational wave observatories using archival seismic data. Classical and Quantum Gravity, 36 (8). Art. No. 085005. ISSN 0264-9381. doi:10.1088/1361-6382/ab0d2c.

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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.

Item Type:Article
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URLURL TypeDescription
Mukund, Nikhil0000-0002-8666-9156
Coughlin, Michael0000-0002-8262-2924
Harms, Jan0000-0002-7332-9806
Biscans, Sebastien0000-0002-9635-7527
Arnaud, Nicolas0000-0001-6589-8673
Swinkels, Bas0000-0002-3066-3601
Additional Information:© 2019 IOP Publishing. Received 14 December 2018, revised 7 February 2019. Accepted for publication 6 March 2019. Published 1 April 2019. NM acknowledges Council for Scientific and Industrial Research (CSIR), India, for providing financial support as Senior Research Fellow. MC was supported by the David and Ellen Lee Postdoctoral Fellowship at the California Institute of Technology. Authors express thanks to Duncan Agnew, Rich Ormiston and Brian O'Reilly for their valuable comments and suggestions. LIGO was constructed by the California Institute of Technology and Massachusetts Institute of Technology with funding from the National Science Foundation and operates under cooperative agreement PHY-0757058. This paper has been assigned LIGO document number LIGO-P1800312. Global Seismographic Network (GSN) is a cooperative scientific facility operated jointly by the Incorporated Research Institutions for Seismology (IRIS), the United States Geological Survey (USGS), and the National Science Foundation (NSF), under Cooperative Agreement EAR-1261681. The facilities of IRIS Data Services and specifically the IRIS Data Management Center were used for access to waveforms, related metadata, and derived products used in this study. IRIS Data Services are funded through the Seismological Facilities for the Advancement of Geoscience and EarthScope (SAGE) Proposal of the National Science Foundation under Cooperative Agreement EAR-126168.
Funding AgencyGrant Number
Council for Scientific and Industrial Research (India)UNSPECIFIED
David and Ellen Lee Postdoctoral ScholarshipUNSPECIFIED
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Other Numbering System NameOther Numbering System ID
LIGO DocumentP1800312
Issue or Number:8
Record Number:CaltechAUTHORS:20190401-113942752
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Official Citation:Nikhil Mukund et al 2019 Class. Quantum Grav. 36 085005
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:94316
Deposited By: George Porter
Deposited On:01 Apr 2019 19:48
Last Modified:12 Jul 2022 19:49

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