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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985530%3A_____%2F22%3A00562267" target="_blank" >RIV/67985530:_____/22:00562267 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0098300422001923" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0098300422001923</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cageo.2022.105243" target="_blank" >10.1016/j.cageo.2022.105243</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10507 - Volcanology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computers and Geosciences

ISSN

0098-3004

e-ISSN

1873-7803

Svazek periodika

169

Číslo periodika v rámci svazku

December

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

9

Strana od-do

105243

Kód UT WoS článku

000882796000001

EID výsledku v databázi Scopus

2-s2.0-85139837852