Examples

On this page you will find links to examples and projects that ultilize GeoST for managing subsurface data.

Retrieving data from the BRO

Extract boreholes and CPTs from the BRO database with just a few lines of code.

./examples/retrieve_bro_data.html

BHR-GT grainsize samples (WIP)

Extract and work with BHR-GT grainsize sample data directly from the BRO

./examples/bhrgt_samples.html

Working with BRO soil cores

A simple analysis of soil cores that are directly retrieved from the BRO.

./examples/bro_soil_cores.html

Combining GeoTOP and CPTs

Combining information from a voxelmodel (GeoTOP) with point data (CPTs).

./examples/combine_geotop_with_cpts.html

Thickness maps from GeoTOP

Create thickness maps of GeoTOP units with only a few lines of code.

./examples/geotop_thickness_maps.html

GeoST + PyVista: 3D export features

Showcasing GeoST’s VTK export and 3D viewing features powered by PyVista.

./examples/boreholes_geotop_3d.html

DIS3.1 Voxelmodel (WIP)

Retrieving DIS3.1 from the Deltares OpenDAP server and basic analysis

./examples/dis_model.html

External examples

Explore the use of GeoST subsurface modelling and data analysis workflows

GeoST + scikit-learn (simple)

Using geological borehole data to predict the thickness of sand using decision tree algorithms provided by scikit-learn.

https://github.com/Deltares-research/sst-examples/blob/main/predict_sand_thickness/predict_sand_thickness_simple.ipynb

GeoST + scikit-learn (advanced)

Using geological borehole data to predict the thickness of sand using decision tree algorithms provided by scikit-learn.

https://github.com/Deltares-research/sst-examples/blob/main/predict_sand_thickness/predict_sand_thickness_advanced.ipynb