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Decomposition and Inference of Sources through Spatiotemporal Analysis of Network Signals: The DISSTANS Python Package
  • Tobias Köhne,
  • Bryan Riel,
  • Mark Simons
Tobias Köhne
California Institute of Technology

Corresponding Author:tkoehne@caltech.edu

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Bryan Riel
Massachusetts Institute of Technology
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Mark Simons
California Institute of Technology
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Regional-scale, continuously-operating Global Navigation Satellite System (GNSS) networks are a powerful tool to monitor plate motion and surface deformation. Since their inception, their size, density, and length of observation record have steadily increased throughout the world. Simultaneously, researchers have had to write accompanying software to enable the analysis (especially the decomposition) of the ever-increasing amount of available timeseries in an efficient way. These codes and respective studies have individually set standards for different subsets of the following desirable qualities: portability (between locations), speed (code runtime), automation (avoiding or simplifying manual inspection of each station), use of spatial correlation (exploiting the fact that stations experience common signals), availability (open source), and documentation (of the usage and underlying methods). In this study, we present the DISSTANS Python package, which aims to combine the aforementioned achievements in a single software by offering generic (therefore portable), parallelizable (fast) methods that can exploit the spatial structure of the observation records in a user-assisted, semi-automated framework, including uncertainty propagation. The code is open source, includes an application interface documentation as well as usage tutorials, is easily extendable, and is based on the previously published and validated method of Riel et al. (2014). We also present two case studies to validate our code, one using a synthetic dataset and one using real GNSS network timeseries.