GPlates and pyGPlates: Open-source software for building a virtual Earth through deep time
Abstract
GPlates (www.gplates.org) is an open-source Plate Tectonic Geographic Information System running on Windows, Linux and MacOSX. It enables the interactive manipulation of plate-tectonic reconstructions and the visualization of geodata through geological time. GPlates has become widely known as a paleo-geographic information system for geoscientists to combine a wide variety of geodata and examine them within tectonic reconstructions through time. The most recent version, GPlates 2.1, contains significant innovations and advances, including volume visualisation and the implementation of plate deformation. The ability to visualize sub-surface 3D scalar fields together with traditional geological surface data enables researchers to observe their relationship through geological time in a common plate tectonic reference frame. This is useful in a variety of scenarios including the visualization of geodynamic models in relation to their plate tectonic surface constraints and visualizing 2D cross-sections of sub-surface data along relevant reconstructed geometrical features. GPlates now enables the definition of deformation zones - regions combining extension, compression and shearing that accommodate the relative motion between rigid blocks that follow more traditional concepts of rigid tectonic plates and Euler pole rotations. Users can explore how strain rates, stretching/shortening factors and crustal thickness evolve through space and time within deformation regions, and interactively update the kinematics associated with deformation to see how these parameters are influenced by alternative scenarios. Where datasets described by geometries (points, lines or polygons) fall within deformation regions, the deformation can be applied to these geometries. PyGPlates, is a Python library that enables access to GPlates functionality via Python scripting. The development of pyGPlates opens the door for connecting spatio-temporal data analysis to "big data" on the cloud. The well-established ecosystem of open-source python-based tools for data-mining, statistics and machine learning can now be linked to pygplates, allowing spatial data to be seamlessly analysed in space and geological "deep time", and with the ability to spread large computations across multiple processors.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFMNS53A0546G
- Keywords:
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- 0520 Data analysis: algorithms and implementation;
- COMPUTATIONAL GEOPHYSICSDE: 0545 Modeling;
- COMPUTATIONAL GEOPHYSICSDE: 0599 General or miscellaneous;
- COMPUTATIONAL GEOPHYSICSDE: 1999 General or miscellaneous;
- INFORMATICS