Soft clustering of GPS velocities from a homogeneous permanent network in Turkey

Ozdemir S., KARSLIOĞLU M. O.

JOURNAL OF GEODESY, vol.93, no.8, pp.1171-1195, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 93 Issue: 8
  • Publication Date: 2019
  • Doi Number: 10.1007/s00190-019-01235-z
  • Journal Name: JOURNAL OF GEODESY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1171-1195
  • Keywords: Soft clustering, Hard clustering, Gaussian mixture model, Velocity field, DEFORMATION, BLOCK, CONSTRAINTS
  • Middle East Technical University Affiliated: Yes


Global positioning system (GPS) velocities have long and widely been used on various scales in revealing the deformations of the continental lithosphere. We present a homogeneous geodetic velocity field with high precision derived from10-year-long permanent GPS observations throughout Turkey. Without any apriori information or assumption, the cluster analysis might be applied upon the velocity fields for inspection, before going further in the analyses used prevalently in tectonic studies. We first hard clustered the velocities using k-means, hierarchical agglomerative clustering and Gaussian mixture models and examined how the cluster assignments change by tuning the algorithm-specific parameters. The Eurasian and the Arabian blocks which are separated from the Anatolian block with the strike-slip North and East Anatolian faults have been detected immediately. The Anatolian block itself has been divided into three blocks where the cluster assignments of the velocities at the transition zones might differ according to the chosen hard clustering algorithm. We then applied soft clustering using an appropriate Gaussian mixture model fit and created a probability map exhibiting the credibility of the cluster assignments. The detection capability of the cluster analysis has been demonstrated by comparison to various previously published block models of western Turkey. Cluster analysis detected the most pronounced blocks in western Turkey successfully, especially when the initially chosen number of clusters is not too large. The probability map of soft clustering can be used to modify the block boundaries together with the external validation.