%0 Journal Article 
%A Álvarez-Ruiz, Daniel
%A Sánchez Alzola, Alberto
%A Pastor Fernández, Andrés
%T Geospatial Clustering of GNSS Stations Using Unsupervised Learning: A Statistical Framework to Enhance Deformation Analysis for Environmental Risk Management
%D 2026
%@ 2227-7390
%U http://hdl.handle.net/10498/39182
%X The global expansion of continuous GNSS networks has generated large-scale spatiotemporal
datasets whose analysis requires robust mathematical and statistical tools. This study
introduces a geospatial, multivariate statistical framework for classifying 21,548 GNSS
stations from the University of Nevada repository. The methodology integrates harmonic
regression, stochastic noise modeling, quality assessment, and slope estimation into a
unified feature space suitable for high-dimensional analysis. Using unsupervised learning
clustering computed with our custom-developed code, based entirely on free and
open-source software, we identify homogeneous station groups that reflect dominant signal
properties—periodicity, noise structure, data quality, and long-term velocity—together
with their spatial context. The resulting clusters exhibit strong mathematical coherence
and reveal continental-scale patterns driven by seasonal forcing, tectonic regime, climatic
variability, and monument stability. By grouping stations with similar statistical behavior,
the proposed framework improves reference-site selection, enhances deformation-field interpretation,
and supports the detection of anomalous or hazard-related behavior. Overall,
this approach provides a scalable, data-driven mathematical tool for analyzing complex
spatiotemporal signals and contributes to more reliable deformation modeling and environmental
risk assessment.
%K machine learning
%K GNSS
%K time series analysis
%K harmonic models
%~ Universidad de Cádiz