Statistics of spatio-temporal processes

Kircher’s model of the seismic systems of the earth

The dynamics of spatial processes evolving in time.



…is a toolkit for high performance geospatial processing, modelling and analysis.

Some highlights of Geostack include:

  • Range of programmable geospatial operations based on OpenCL, including map algebra, distance mapping and rasterisation.
  • Data IO for common geospatial types such as geotiff and shapefiles with no dependencies.
  • Implicit handling geospatial alignment and projections, allowing easier coding of geospatial models.
  • Python bindings for interoperability with GDAL/RasterIO/xarray/NetCDF.
  • Built-in computational solvers including level set and network flow models.

More information and build guides are on our wiki.

Geostack can be installed for Python using conda.

gstat does certain R stats.


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Peruzzi, Michele, Sudipto Banerjee, and Andrew O. Finley. 2020. Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains.” Journal of the American Statistical Association 0 (0): 1–14.
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Roberts, Dale, Norman Mueller, and Alexis Mcintyre. 2017. High-Dimensional Pixel Composites From Earth Observation Time Series.” IEEE Transactions on Geoscience and Remote Sensing 55 (11): 6254–64.
Scalzo, Richard, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps. 2022. Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models.” Geoscientific Model Development 15 (9): 3641–62.
Sun, Alexander Y., Hongkyu Yoon, Chung-Yan Shih, and Zhi Zhong. 2021. Applications of Physics-Informed Scientific Machine Learning in Subsurface Science: A Survey.” arXiv:2104.04764 [Physics], April.

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