Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults
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...
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| Veröffentlicht in: | Earth and space science (Hoboken, N.J.) N.J.), 2021-11, Vol.8 (11), p.e2021EA001680-n/a |
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| creator | Granat, Robert Donnellan, Andrea Heflin, Michael Lyzenga, Gregory Glasscoe, Margaret Parker, Jay Pierce, Marlon Wang, Jun Rundle, John Ludwig, Lisa G. |
| description | 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.
Key Points
Unsupervised clustering methods provide a data‐driven way of analyzing and partitioning Global Navigation Satellite System observations of crustal deformation
Deformation is distributed across the San Andreas fault system but is localized at the creeping section in central California
The Southern San Andreas fault connects with the Eastern California Shear Zone rather than the rest of the San Andreas fault system |
| doi_str_mv | 10.1029/2021EA001680 |
| format | Article |
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Key Points
Unsupervised clustering methods provide a data‐driven way of analyzing and partitioning Global Navigation Satellite System observations of crustal deformation
Deformation is distributed across the San Andreas fault system but is localized at the creeping section in central California
The Southern San Andreas fault connects with the Eastern California Shear Zone rather than the rest of the San Andreas fault system</description><identifier>ISSN: 2333-5084</identifier><identifier>EISSN: 2333-5084</identifier><identifier>DOI: 10.1029/2021EA001680</identifier><identifier>PMID: 34820480</identifier><language>eng</language><publisher>United States: John Wiley & Sons, Inc</publisher><subject>Abrupt/Rapid Climate Change ; Air/Sea Constituent Fluxes ; Air/Sea Interactions ; Algorithms ; Atmospheric ; Atmospheric Composition and Structure ; Atmospheric Effects ; Atmospheric Processes ; Avalanches ; Benefit‐cost Analysis ; Biogeosciences ; Boundaries ; Climate and Interannual Variability ; Climate Change and Variability ; Climate Dynamics ; Climate Impact ; Climate Impacts ; Climate Variability ; Climatology ; Clustering ; Computational Geophysics ; Continental Crust ; Cryosphere ; Decadal Ocean Variability ; Design ; Disaster Risk Analysis and Assessment ; Earth System Modeling ; earthquake ; Earthquake Dynamics ; Earthquake Ground Motions and Engineering Seismology ; Earthquake Interaction, Forecasting, and Prediction ; Earthquake Source Observations ; Earthquakes ; Effusive Volcanism ; Estimation and Forecasting ; Experiments ; Exploration Geophysics ; Explosive Volcanism ; Fault lines ; faults ; Forecasting ; General Circulation ; Geodesy and Gravity ; geodetic imaging ; Geological ; Geometry ; Global Change ; Global Change from Geodesy ; GNSS ; Gravity and Isostasy ; Gravity anomalies and Earth structure ; Gravity Methods ; Hydrological Cycles and Budgets ; Hydrology ; Impacts of Global Change ; Informatics ; Instruments and Techniques ; Interferometry ; Ionosphere ; Ionospheric Physics ; Land/Atmosphere Interactions ; Machine learning for Solid Earth observation, modeling and understanding ; Magnetospheric Physics ; Marine Geology and Geophysics ; Mass Balance ; Mathematical Geophysics ; Modeling ; Monitoring, Forecasting, Prediction ; Mud Volcanism ; Multihazards ; Natural Hazards ; Numerical Modeling ; Numerical Solutions ; Ocean influence of Earth rotation ; Ocean Monitoring with Geodetic Techniques ; Ocean Predictability and Prediction ; Ocean/Atmosphere Interactions ; Ocean/Earth/atmosphere/hydrosphere/cryosphere interactions ; Oceanic ; Oceanography: General ; Oceanography: Physical ; Oceans ; Paleoceanography ; Physical Modeling ; Policy ; Policy Sciences ; Prediction ; Probabilistic Forecasting ; Radio Oceanography ; Radio Science ; Regional Climate Change ; Regional Modeling ; Risk ; Satellite Geodesy: Results ; Sea Level Change ; Sea Level: Variations and Mean ; Seismic activity ; Seismic Cycle Related Deformations ; Seismicity and Tectonics ; Seismology ; Software ; Solid Earth ; Space Weather ; Subduction Zones ; Surface Waves and Tides ; Technical Reports: Methods ; Tectonic Deformation ; tectonics ; Theoretical Modeling ; Time Variable Gravity ; Transient Deformation ; Tsunamis and Storm Surges ; Velocity ; Volcanic Effects ; Volcanic Hazards and Risks ; Volcano Monitoring ; Volcano Seismology ; Volcano/Climate Interactions ; Volcanology ; Water Cycles ; Workflow</subject><ispartof>Earth and space science (Hoboken, N.J.), 2021-11, Vol.8 (11), p.e2021EA001680-n/a</ispartof><rights>2021 Jet Propulsion Laboratory, California Institute of Technology. Government sponsorship acknowledged.</rights><rights>2021. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). 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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.
Key Points
Unsupervised clustering methods provide a data‐driven way of analyzing and partitioning Global Navigation Satellite System observations of crustal deformation
Deformation is distributed across the San Andreas fault system but is localized at the creeping section in central California
The Southern San Andreas fault connects with the Eastern California Shear Zone rather than the rest of the San Andreas fault system</description><subject>Abrupt/Rapid Climate Change</subject><subject>Air/Sea Constituent Fluxes</subject><subject>Air/Sea Interactions</subject><subject>Algorithms</subject><subject>Atmospheric</subject><subject>Atmospheric Composition and Structure</subject><subject>Atmospheric Effects</subject><subject>Atmospheric Processes</subject><subject>Avalanches</subject><subject>Benefit‐cost Analysis</subject><subject>Biogeosciences</subject><subject>Boundaries</subject><subject>Climate and Interannual Variability</subject><subject>Climate Change and Variability</subject><subject>Climate Dynamics</subject><subject>Climate Impact</subject><subject>Climate Impacts</subject><subject>Climate Variability</subject><subject>Climatology</subject><subject>Clustering</subject><subject>Computational Geophysics</subject><subject>Continental 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Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults</title><author>Granat, Robert ; Donnellan, Andrea ; Heflin, Michael ; Lyzenga, Gregory ; Glasscoe, Margaret ; Parker, Jay ; Pierce, Marlon ; Wang, Jun ; Rundle, John ; Ludwig, Lisa G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5205-7f09cb4af117e14242405d1828d9192a1ef51d3b52d055ab275c897cafdf9d453</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Abrupt/Rapid Climate Change</topic><topic>Air/Sea Constituent Fluxes</topic><topic>Air/Sea Interactions</topic><topic>Algorithms</topic><topic>Atmospheric</topic><topic>Atmospheric Composition and Structure</topic><topic>Atmospheric Effects</topic><topic>Atmospheric Processes</topic><topic>Avalanches</topic><topic>Benefit‐cost Analysis</topic><topic>Biogeosciences</topic><topic>Boundaries</topic><topic>Climate and 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and Mean</topic><topic>Seismic activity</topic><topic>Seismic Cycle Related Deformations</topic><topic>Seismicity and Tectonics</topic><topic>Seismology</topic><topic>Software</topic><topic>Solid Earth</topic><topic>Space Weather</topic><topic>Subduction Zones</topic><topic>Surface Waves and Tides</topic><topic>Technical Reports: Methods</topic><topic>Tectonic Deformation</topic><topic>tectonics</topic><topic>Theoretical Modeling</topic><topic>Time Variable Gravity</topic><topic>Transient Deformation</topic><topic>Tsunamis and Storm Surges</topic><topic>Velocity</topic><topic>Volcanic Effects</topic><topic>Volcanic Hazards and Risks</topic><topic>Volcano Monitoring</topic><topic>Volcano Seismology</topic><topic>Volcano/Climate Interactions</topic><topic>Volcanology</topic><topic>Water Cycles</topic><topic>Workflow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Granat, Robert</creatorcontrib><creatorcontrib>Donnellan, Andrea</creatorcontrib><creatorcontrib>Heflin, Michael</creatorcontrib><creatorcontrib>Lyzenga, Gregory</creatorcontrib><creatorcontrib>Glasscoe, Margaret</creatorcontrib><creatorcontrib>Parker, Jay</creatorcontrib><creatorcontrib>Pierce, Marlon</creatorcontrib><creatorcontrib>Wang, Jun</creatorcontrib><creatorcontrib>Rundle, John</creatorcontrib><creatorcontrib>Ludwig, Lisa G.</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest Central (New)</collection><collection>ProQuest One Academic (New)</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Middle East (New)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Earth and space science (Hoboken, N.J.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Granat, Robert</au><au>Donnellan, Andrea</au><au>Heflin, Michael</au><au>Lyzenga, Gregory</au><au>Glasscoe, Margaret</au><au>Parker, Jay</au><au>Pierce, Marlon</au><au>Wang, Jun</au><au>Rundle, John</au><au>Ludwig, Lisa G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults</atitle><jtitle>Earth and space science (Hoboken, N.J.)</jtitle><addtitle>Earth Space Sci</addtitle><date>2021-11</date><risdate>2021</risdate><volume>8</volume><issue>11</issue><spage>e2021EA001680</spage><epage>n/a</epage><pages>e2021EA001680-n/a</pages><issn>2333-5084</issn><eissn>2333-5084</eissn><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.
Key Points
Unsupervised clustering methods provide a data‐driven way of analyzing and partitioning Global Navigation Satellite System observations of crustal deformation
Deformation is distributed across the San Andreas fault system but is localized at the creeping section in central California
The Southern San Andreas fault connects with the Eastern California Shear Zone rather than the rest of the San Andreas fault system</abstract><cop>United States</cop><pub>John Wiley & Sons, Inc</pub><pmid>34820480</pmid><doi>10.1029/2021EA001680</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0001-6977-2189</orcidid><orcidid>https://orcid.org/0000-0002-7350-4379</orcidid><orcidid>https://orcid.org/0000-0001-6792-1515</orcidid><orcidid>https://orcid.org/0000-0002-8457-1235</orcidid><orcidid>https://orcid.org/0000-0001-6843-8373</orcidid><orcidid>https://orcid.org/0000-0001-6538-8067</orcidid><orcidid>https://orcid.org/0000-0002-9535-7170</orcidid><orcidid>https://orcid.org/0000-0002-4408-2517</orcidid><orcidid>https://orcid.org/0000-0002-1966-4144</orcidid><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 2333-5084 |
| ispartof | Earth and space science (Hoboken, N.J.), 2021-11, Vol.8 (11), p.e2021EA001680-n/a |
| issn | 2333-5084 2333-5084 |
| language | eng |
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| subjects | Abrupt/Rapid Climate Change Air/Sea Constituent Fluxes Air/Sea Interactions Algorithms Atmospheric Atmospheric Composition and Structure Atmospheric Effects Atmospheric Processes Avalanches Benefit‐cost Analysis Biogeosciences Boundaries Climate and Interannual Variability Climate Change and Variability Climate Dynamics Climate Impact Climate Impacts Climate Variability Climatology Clustering Computational Geophysics Continental Crust Cryosphere Decadal Ocean Variability Design Disaster Risk Analysis and Assessment Earth System Modeling earthquake Earthquake Dynamics Earthquake Ground Motions and Engineering Seismology Earthquake Interaction, Forecasting, and Prediction Earthquake Source Observations Earthquakes Effusive Volcanism Estimation and Forecasting Experiments Exploration Geophysics Explosive Volcanism Fault lines faults Forecasting General Circulation Geodesy and Gravity geodetic imaging Geological Geometry Global Change Global Change from Geodesy GNSS Gravity and Isostasy Gravity anomalies and Earth structure Gravity Methods Hydrological Cycles and Budgets Hydrology Impacts of Global Change Informatics Instruments and Techniques Interferometry Ionosphere Ionospheric Physics Land/Atmosphere Interactions Machine learning for Solid Earth observation, modeling and understanding Magnetospheric Physics Marine Geology and Geophysics Mass Balance Mathematical Geophysics Modeling Monitoring, Forecasting, Prediction Mud Volcanism Multihazards Natural Hazards Numerical Modeling Numerical Solutions Ocean influence of Earth rotation Ocean Monitoring with Geodetic Techniques Ocean Predictability and Prediction Ocean/Atmosphere Interactions Ocean/Earth/atmosphere/hydrosphere/cryosphere interactions Oceanic Oceanography: General Oceanography: Physical Oceans Paleoceanography Physical Modeling Policy Policy Sciences Prediction Probabilistic Forecasting Radio Oceanography Radio Science Regional Climate Change Regional Modeling Risk Satellite Geodesy: Results Sea Level Change Sea Level: Variations and Mean Seismic activity Seismic Cycle Related Deformations Seismicity and Tectonics Seismology Software Solid Earth Space Weather Subduction Zones Surface Waves and Tides Technical Reports: Methods Tectonic Deformation tectonics Theoretical Modeling Time Variable Gravity Transient Deformation Tsunamis and Storm Surges Velocity Volcanic Effects Volcanic Hazards and Risks Volcano Monitoring Volcano Seismology Volcano/Climate Interactions Volcanology Water Cycles Workflow |
| title | Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults |
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