Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults

Abstract We present a data‐driven approach to clustering or grouping Global Navigation Satellite System (GNSS) stations according to observed velocities, displacements or other selected characteristics. Clustering GNSS stations provides useful scientific information, and is a necessary initial step...

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Autores principales:	Robert Granat, Andrea Donnellan, Michael Heflin, Gregory Lyzenga, Margaret Glasscoe, Jay Parker, Marlon Pierce, Jun Wang, John Rundle, Lisa G. Ludwig
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2021
Materias:	clustering geodetic imaging tectonics GNSS faults earthquake Astronomy QB1-991 Geology QE1-996.5
Acceso en línea:	https://doaj.org/article/f5ed864ab8614549a411c94e31e1128e
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spelling	oai:doaj.org-article:f5ed864ab8614549a411c94e31e1128e2021-11-23T21:03:09ZClustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults2333-508410.1029/2021EA001680https://doaj.org/article/f5ed864ab8614549a411c94e31e1128e2021-11-01T00:00:00Zhttps://doi.org/10.1029/2021EA001680https://doaj.org/toc/2333-5084Abstract We present a data‐driven approach to clustering or grouping Global Navigation Satellite System (GNSS) stations according to observed velocities, displacements or other selected characteristics. Clustering GNSS stations provides useful scientific information, and is a necessary initial step in other analysis, such as detecting aseismic transient signals (Granat et al., 2013, https://doi.org/10.1785/0220130039). Desired features of the data can be selected for clustering, including some subset of displacement or velocity components, uncertainty estimates, station location, and other relevant information. Based on those selections, the clustering procedure autonomously groups the GNSS stations according to a selected clustering method. We have implemented this approach as a Python application, allowing us to draw upon the full range of open source clustering methods available in Python's scikit‐learn package (Pedregosa et al., 2011, https://doi.org/10.5555/1953048.2078195). The application returns the stations labeled by group as a table and color coded KML file and is designed to work with the GNSS information available from GeoGateway (Donnellan et al., 2021, https://doi.org/10.1007/s12145-020-00561-7; Heflin et al., 2020, https://doi.org/10.1029/2019ea000644) but is easily extensible. We demonstrate the methodology on California and western Nevada. The results show partitions that follow faults or geologic boundaries, including for recent large earthquakes and post‐seismic motion. The San Andreas fault system is most prominent, reflecting Pacific‐North American plate boundary motion. Deformation reflected as class boundaries is distributed north and south of the central California creeping section. For most models a cluster boundary connects the southernmost San Andreas fault with the Eastern California Shear Zone (ECSZ) rather than continuing through the San Gorgonio Pass.Robert GranatAndrea DonnellanMichael HeflinGregory LyzengaMargaret GlasscoeJay ParkerMarlon PierceJun WangJohn RundleLisa G. LudwigAmerican Geophysical Union (AGU)articleclusteringgeodetic imagingtectonicsGNSSfaultsearthquakeAstronomyQB1-991GeologyQE1-996.5ENEarth and Space Science, Vol 8, Iss 11, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	clustering geodetic imaging tectonics GNSS faults earthquake Astronomy QB1-991 Geology QE1-996.5
spellingShingle	clustering geodetic imaging tectonics GNSS faults earthquake Astronomy QB1-991 Geology QE1-996.5 Robert Granat Andrea Donnellan Michael Heflin Gregory Lyzenga Margaret Glasscoe Jay Parker Marlon Pierce Jun Wang John Rundle Lisa G. Ludwig Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults
description	Abstract We present a data‐driven approach to clustering or grouping Global Navigation Satellite System (GNSS) stations according to observed velocities, displacements or other selected characteristics. Clustering GNSS stations provides useful scientific information, and is a necessary initial step in other analysis, such as detecting aseismic transient signals (Granat et al., 2013, https://doi.org/10.1785/0220130039). Desired features of the data can be selected for clustering, including some subset of displacement or velocity components, uncertainty estimates, station location, and other relevant information. Based on those selections, the clustering procedure autonomously groups the GNSS stations according to a selected clustering method. We have implemented this approach as a Python application, allowing us to draw upon the full range of open source clustering methods available in Python's scikit‐learn package (Pedregosa et al., 2011, https://doi.org/10.5555/1953048.2078195). The application returns the stations labeled by group as a table and color coded KML file and is designed to work with the GNSS information available from GeoGateway (Donnellan et al., 2021, https://doi.org/10.1007/s12145-020-00561-7; Heflin et al., 2020, https://doi.org/10.1029/2019ea000644) but is easily extensible. We demonstrate the methodology on California and western Nevada. The results show partitions that follow faults or geologic boundaries, including for recent large earthquakes and post‐seismic motion. The San Andreas fault system is most prominent, reflecting Pacific‐North American plate boundary motion. Deformation reflected as class boundaries is distributed north and south of the central California creeping section. For most models a cluster boundary connects the southernmost San Andreas fault with the Eastern California Shear Zone (ECSZ) rather than continuing through the San Gorgonio Pass.
format	article
author	Robert Granat Andrea Donnellan Michael Heflin Gregory Lyzenga Margaret Glasscoe Jay Parker Marlon Pierce Jun Wang John Rundle Lisa G. Ludwig
author_facet	Robert Granat Andrea Donnellan Michael Heflin Gregory Lyzenga Margaret Glasscoe Jay Parker Marlon Pierce Jun Wang John Rundle Lisa G. Ludwig
author_sort	Robert Granat
title	Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults
title_short	Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults
title_full	Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults
title_fullStr	Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults
title_full_unstemmed	Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults
title_sort	clustering analysis methods for gnss observations: a data‐driven approach to identifying california's major faults
publisher	American Geophysical Union (AGU)
publishDate	2021
url	https://doaj.org/article/f5ed864ab8614549a411c94e31e1128e
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Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults

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