Deep time spatio‐temporal data analysis using pyGPlates with PlateTectonicTools and GPlately

Author:

Mather Ben R.1ORCID,Müller R. Dietmar1,Zahirovic Sabin1ORCID,Cannon John1ORCID,Chin Michael1,Ilano Lauren1,Wright Nicky M.1,Alfonso Christopher1,Williams Simon2,Tetley Michael3,Merdith Andrew4

Affiliation:

1. EarthByte Group, School of Geosciences The University of Sydney Sydney New South Wales Australia

2. School of Geoscience Northwest University Xi'an China

3. University of Texas Institute for Geophysics Austin Texas USA

4. School of Earth and Environment University of Leeds Leeds UK

Abstract

AbstractPyGPlates is an open‐source Python library to visualize and edit plate tectonic reconstructions created using GPlates. The Python API affords a greater level of flexibility than GPlates to interrogate plate reconstructions and integrate with other Python workflows. GPlately was created to accelerate spatio‐temporal data analysis leveraging pyGPlates and PlateTectonicTools within a simplified Python interface. This object‐oriented package enables the reconstruction of data through deep geologic time (points, lines, polygons and rasters), the interrogation of plate kinematic information (plate velocities, rates of subduction and seafloor spreading), the rapid comparison between multiple plate motion models, and the plotting of reconstructed output data on maps. All tools are designed to be parallel‐safe to accelerate spatio‐temporal analysis over multiple CPU processors.

Funder

Australian Research Council

Publisher

Wiley

Subject

General Earth and Planetary Sciences

Reference19 articles.

1. GPlates: Building a Virtual Earth Through Deep Time

2. A Global Plate Model Including Lithospheric Deformation Along Major Rifts and Orogens Since the Triassic

3. Gillies S.(2013)Rasterio: geospatial raster i/o for python programmers. Available from:https://github.com/rasterio/rasterioa

4. Gillies S. van derWel C. Van den Bossche J. Taves M.W. Arnott J. Ward B.C.et al. (2021)Shapely v1.8. Available from:https://doi.org/10.5281/zenodo.7263102

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