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

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  • 1City College of New York New York NY USA.

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Summary
This summary is machine-generated.

This study introduces a data-driven Python application for clustering Global Navigation Satellite System (GNSS) stations. The method groups stations by velocity and displacement, revealing tectonic plate boundary motions and fault systems.

Keywords:
GNSSclusteringearthquakefaultsgeodetic imagingtectonics

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Area of Science:

  • Geodesy
  • Geophysics
  • Data Science

Background:

  • Clustering Global Navigation Satellite System (GNSS) stations is crucial for scientific analysis, including detecting aseismic transient signals.
  • Existing methods may lack flexibility in selecting clustering features and integrating diverse data sources.

Purpose of the Study:

  • To develop a data-driven, flexible Python application for clustering GNSS stations based on selected characteristics.
  • To provide a tool that autonomously groups GNSS stations using various clustering algorithms.

Main Methods:

  • Implemented a Python application leveraging scikit-learn for clustering GNSS station data.
  • Enabled selection of features such as displacement, velocity components, and uncertainty estimates.
  • Integrated with GeoGateway for accessing GNSS data and outputting results as tables and KML files.

Main Results:

  • Demonstrated the methodology on GNSS data from California and western Nevada.
  • Identified distinct clusters corresponding to major fault systems, including the San Andreas fault system.
  • Observed deformation patterns reflecting Pacific-North American plate boundary motion and post-seismic activity.

Conclusions:

  • The developed application effectively clusters GNSS stations, providing insights into tectonic processes.
  • Cluster boundaries align with known geologic structures and deformation patterns, validating the approach.
  • The tool is extensible and valuable for geophysical analysis and understanding crustal deformation.