A Caltech Library Service

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

Köhne, Tobias and Riel, Bryan and Simons, Mark (2023) Decomposition and Inference of Sources through Spatiotemporal Analysis of Network Signals: The DISSTANS Python package. Computers and Geosciences, 170 . Art. No. 105247. ISSN 0098-3004. doi:10.1016/j.cageo.2022.105247.

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item:


Dense, regional-scale, continuously-operating Global Navigation Satellite System (GNSS) networks enable the monitoring of plate motion and regional 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 efficiently analyze the ever-increasing amount of available timeseries should be geographically portable and computationally efficient, allow for automation, use spatial correlation (exploiting the fact that nearby stations experience common signals), and have openly accessible source code as well as 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 that includes uncertainty propagation. DISSTANS 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.

Item Type:Article
Related URLs:
URLURL TypeDescription
Köhne, Tobias0000-0002-8400-7255
Riel, Bryan0000-0003-1940-3910
Simons, Mark0000-0003-1412-6395
Additional Information:This work has been partially supported through a collaboration with the King Abdulaziz City for Science and Technology (KACST), Saudi Arabia.
Group:Division of Geological and Planetary Sciences, Seismological Laboratory
Funding AgencyGrant Number
King Abdulaziz City for Science and Technology (KACST)UNSPECIFIED
Record Number:CaltechAUTHORS:20221116-601553800.12
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117891
Deposited By: Research Services Depository
Deposited On:29 Nov 2022 16:04
Last Modified:29 Nov 2022 16:04

Repository Staff Only: item control page