Clustering Analysis Methods for GNSS Observations: A Data‐Driven Approach to Identifying California's Major Faults
| Autor: | Michael Heflin, Andrea Donnellan, Robert Granat, Gregory A. Lyzenga, Margaret Glasscoe, John B. Rundle, Lisa Grant Ludwig, Marlon Pierce, Jun Wang, Jay Parker |
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| Rok vydání: | 2021 |
| Předmět: |
Earthquake Source Observations
Biogeosciences Volcanic Effects Global Change from Geodesy Ionospheric Physics Volcanic Hazards and Risks Oceans Sea Level Change Disaster Risk Analysis and Assessment Earthquake Interaction Forecasting and Prediction computer.programming_language QE1-996.5 Gravity Methods Climate and Interannual Variability Geology faults Seismic Cycle Related Deformations Tectonic Deformation Climate Impact Earthquake Ground Motions and Engineering Seismology Explosive Volcanism Time Variable Gravity Earth System Modeling Atmospheric Processes Seismicity and Tectonics Shear zone Ocean Monitoring with Geodetic Techniques Ocean/Atmosphere Interactions Mathematical Geophysics Atmospheric Probabilistic Forecasting Regional Modeling Atmospheric Effects Volcanology Satellite system Hydrological Cycles and Budgets Decadal Ocean Variability Land/Atmosphere Interactions Earthquake Dynamics Magnetospheric Physics Geodesy and Gravity Global Change Air/Sea Interactions Numerical Modeling Solid Earth Gravity anomalies and Earth structure Geological Ocean/Earth/atmosphere/hydrosphere/cryosphere interactions GNSS Water Cycles Modeling Avalanches Volcano Seismology Python (programming language) Benefit‐cost Analysis Tectonics GNSS applications earthquake Computational Geophysics Regional Climate Change Subduction Zones Transient Deformation Natural Hazards Abrupt/Rapid Climate Change Informatics Astronomy Surface Waves and Tides Boundary (topology) Atmospheric Composition and Structure Volcano Monitoring Workflow Instruments and Techniques Seismology Climatology Exploration Geophysics Ocean Predictability and Prediction Radio Oceanography Gravity and Isostasy Marine Geology and Geophysics Geodesy Physical Modeling Oceanography: General Policy Estimation and Forecasting Space Weather Cryosphere Impacts of Global Change Oceanography: Physical clustering Risk Oceanic Theoretical Modeling Satellite Geodesy: Results Technical Reports: Methods QB1-991 Environmental Science (miscellaneous) Radio Science Data-driven Tsunamis and Storm Surges Paleoceanography Climate Dynamics tectonics Ionosphere Monitoring Forecasting Prediction Cluster analysis Numerical Solutions Climate Change and Variability Continental Crust Multihazards Effusive Volcanism geodetic imaging Climate Variability General Circulation Policy Sciences Climate Impacts Machine learning for Solid Earth observation modeling and understanding Mud Volcanism Air/Sea Constituent Fluxes Mass Balance Interferometry Ocean influence of Earth rotation Volcano/Climate Interactions General Earth and Planetary Sciences Hydrology Prediction Sea Level: Variations and Mean computer Forecasting |
| Zdroj: | Earth and Space Science, vol 8, iss 11 Earth and Space Science, Vol 8, Iss 11, Pp n/a-n/a (2021) Earth and Space Science (Hoboken, N.j.) |
| ISSN: | 2333-5084 |
| DOI: | 10.1029/2021ea001680 |
| Popis: | 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 deformationDeformation is distributed across the San Andreas fault system but is localized at the creeping section in central CaliforniaThe Southern San Andreas fault connects with the Eastern California Shear Zone rather than the rest of the San Andreas fault system |
| Databáze: | OpenAIRE |
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