Decomposition and Inference of Sources through Spatiotemporal Analysis of Network Signals: The DISSTANS Python package

Abstract

Dense, regional-scale, continuously-operating Global Navigation Satellite System (GNSS) networks are powerful tools to monitor plate motion and surface deformation. The spatial extent and density of these networks, as well as the length of observation records, have steadily increased in the past three decades.Software to enable the efficient analysis (especially the decomposition) of the ever-increasing amount of available timeseries should have the following desirable qualities: geographic portability, computational speed, automation (minimizing the need for manual inspection of each station), use of spatial correlation (exploiting the fact that stations experience common signals), source code availability, and documentation.We introduce the DISSTANS Python package, which aims to be generic (therefore portable), parallelizable (fast), and able to 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, and is easily extendable.We present two case studies that demonstrate our code, one using a synthetic dataset and one using real GNSS network timeseries.