Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults. Issue 11 (29th October 2021)

• Record Type: Journal Article
• Authors: Granat, Robert; Donnellan, Andrea; Heflin, Michael; Lyzenga, Gregory; Glasscoe, Margaret; Parker, Jay; Pierce, Marlon; Wang, Jun; Rundle, John; Ludwig, Lisa G.
• Is Part Of: Earth and space science. Volume 8:Issue 11(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-10-29
• Subjects: clustering -- geodetic imaging -- tectonics -- GNSS -- faults -- earthquake; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2021EA001680
• Languages: English
• ISSNs: 2333-5084

Abstract

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