On analyzing GNSS displacement field variability of Taiwan: Hierarchical Agglomerative Clustering based on Dynamic Time Warping technique

• Published in: Computers & geosciences 2022-12, Vol.169, p.105243, Article 105243
• Main Authors: Kumar, Utpal, Legendre, Cédric P., Lee, Jian-Cheng, Zhao, Li, Chao, Benjamin Fong
• Format: Article
• Language: English
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2022.105243

We investigate the feasibility of using the Dynamic Time Warping (DTW) technique to cluster continuous GNSS displacements in Taiwan. Using the DTW distance as the measure for waveform similarity, we combine the DTW method with the Hierarchical Agglomerative Clustering (HAC) algorithm. This is in contrast to the conventional clustering approach that uses the Euclidean distance, considering the average long-term crustal motion, but inherently neglects full-waveform temporal variations. Here we apply the DTW-based HAC algorithm adopting DTW distance as the waveform similarity measure on 11 years worth of 3-D displacement data from 115 continuous GNSS network stations in Taiwan. We demonstrate the efficacy of the DTW-based HAC method in distinguishing the GNSS spatiotemporal variabilities that are consistent with the known, complex tectonic behavior of the region. An open-source Python package has been developed and made available to perform the HAC analysis. •Dynamic Time Warping (DTW) based Hierarchical Agglomerative Clustering (HAC) technique is highly effective in clustering the geodetic time-series data.•We apply the DTW-based HAC algorithm adopting DTW distance as the waveform similarity metric on 11 years' worth of 3-D data from 115 continuous GNSS network stations in Taiwan.•DTW effectively captures the patterns of distinct tectonic motions and potential incoherency in GNSS displacement time series.•An open-source Python package, dtwhaclusteringto perform the HAC analysis has been developed and made available..