Regional Global Navigation Satellite System Networks for Crustal Deformation Monitoring

Autor: M. H. Murray, Valerie Thomas, Robert Smalley, David Mencin, Jessica R. Murray, Ingrid A. Johanson, James Foster, Benjamin A. Brooks, Glen Mattioli, Yehuda Bock, Noel Bartlow, D. Mann, William C. Hammond, Jeffrey T. Freymueller, Kathleen Hodgkinson, Alberto López-Venegas, E. K. Montgomery-Brown, Timothy I. Melbourne
Rok vydání: 2019
Předmět:
Zdroj: Seismological Research Letters. 91:552-572
ISSN: 1938-2057
0895-0695
Popis: Regional networks of Global Navigation Satellite System (GNSS) stations cover seismically and volcanically active areas throughout the United States. Data from these networks have been used to produce high-precision, three-component velocity fields covering broad geographic regions as well as position time series that track time-varying crustal deformation. This information has contributed to assessing interseismic strain accumulation and related seismic hazard, revealed previously unknown occurrences of aseismic fault slip, constrained coseismic slip estimates, and enabled monitoring of volcanic unrest and postseismic deformation. In addition, real-time GNSS data are now widely available. Such observations proved invaluable for tracking the rapidly evolving eruption of Kīlauea in 2018. Real-time earthquake source modeling using GNSS data is being incorporated into tsunami warning systems, and a vigorous research effort is focused on quantifying the contribution that real-time GNSS can make to improve earthquake early warnings as part of the Advanced National Seismic System ShakeAlert system. Real-time GNSS data can also aid in the tracking of ionospheric disturbances and precipitable water vapor for weather forecasting. Although regional GNSS and seismic networks generally have been established independently, their spatial footprints often overlap, and in some cases the same institution operates both types of networks. Further integration of GNSS and seismic networks would promote joint use of the two data types to better characterize earthquake sources and ground motion as well as offer opportunities for more efficient network operations. Looking ahead, upgrading network stations to leverage new GNSS technology could enable more precise positioning and robust real-time operations. New computational approaches such as machine learning have the potential to enable full utilization of the large amounts of data generated by continuous GNSS networks. Development of seafloor Global Positioning System-acoustic networks would provide unique information for fundamental and applied research on subduction zone seismic hazard and, potentially, monitoring.
Databáze: OpenAIRE
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