Geostatistical Learning
2021-07-28, 12:30–13:00 (UTC), Green

Geostatistical Learning is a new branch of Geostatistics concerned with learning functions over geospatial domains (e.g. 2D maps, 3D subsurface models). The theory is being carefully implemented in the GeoStats.jl framework, which is an extensible framework for high-performance geostatistics in Julia. In this talk, I will illustrate how the framework can be used to learn functions over general unstructured meshes, and how this unique technology can help advance geoscientific work.


The theory was introduced in our recent (open access) paper available online: https://www.frontiersin.org/articles/10.3389/fams.2021.689393/full

Its implementation requires knowledge of geostatistics, computational geometry, and high-performance computing. Due to the great features of the Julia language we were able to achieve an elegant design with great runtime performance.

Packages: GeoStats.jl, Meshes.jl

Dr. Júlio Hoffimann is a research scientist with more than 10 years of experience in advanced statistical theories for geosciences. He is the author and lead developer of the GeoStats.jl framework, as well as various other open source projects that are widely used by geoscientists around the world: https://juliohm.github.io