Deforming numerical rasters

Hi!

In GPlates it’s possible to cut a raster into polygons that can then be reconstructed to their past position. However, the region I work on (SE Tibet) is not a rigid polygon but a region that has been subject to a large amount of contraction in the Cenozoic (hence the deforming topologies in Muller 2019 model). Is it also possible to cut a numerical raster into deforming topologies and restore individual points of that raster to their former location within the deforming topology? The points of the numerical raster would be treated similar as a deforming mesh in that case.

Cheers,
Thomas

Hi Thomas,

That’s planned for a future GPlates release. It’s a bit more tricky than rigid raster reconstruction in terms of the way it’s implemented with graphics hardware acceleration (ie, the reason rigid raster reconstruction is fast enough to be interactive, which we’d like to retain for deforming rasters).

However, before that’s implemented in GPlates, it’s more likely that this will be done using Python libraries built on top of pyGPlates (see this post for more details on two libraries currently in development). That’s because the core approach taken in those libraries is to reconstruct points, and that will work quite well with the upcoming deforming functionality (in the next pyGPlates release). Essentially those libraries currently work by taking a uniform grid of points, sampling a present day raster (to give each point a z value), reconstructing those points to a past time and then creating a new raster using those reconstructed points and their z values. So the same approach can still apply when the points are deformed.

Hi John,

Thank you so much for that swift response!
Those libraries would do indeed exactly what I envisaged for my numerical raster, I will have a look at them!

Cheers,
Thomas