About GeoST

Subsurface data are at the basis of addressing challenges related to levee safety, infrastructure stability, drinking water extraction, (sand) mining, subsurface energy systems, archaeology, CO₂ storage, subsidence, contamination, and many other topics. Experts use subsurface data to advise on solutions, visualize and interpret the subsurface in the context of these challenges, and, most importantly, create tailor-made schematizations of the subsurface that directly serve as input for calculating and mapping stability, subsidence, groundwater flow, thermal properties, subsurface resources, and more.

These often complex and high-stakes problems require robust, reproducible, and thoroughly tested workflows, from subsurface data sources (BRO, GDNR, local files) to, for example, 3D subsurface models. Therefore, Deltares started developing GeoST as a tool to support the use of subsurface data in Python. It provides convenient ways to access, process, and combine many different sources of subsurface data by offering frequently used (spatial) selection functionalities, analytical solutions, and methods for reading and exporting data to industry-standard third-party software.

As shown in the figure below, GeoST aims to bridge the gap between subsurface data sources and the analyses, visualisation, and modelling efforts that follow. Simple visualisations and analyses can be used directly to support expert advice, while subsurface data can also be used to create more advanced schematizations and models that serve as input for applied models.

Design philosophy

The design of GeoST can best be described as an hourglass. At the top of this hourglass are the many different sources through which subsurface data are distributed. This information is read, parsed, validated, and subsequently stored in standardized data objects (the neck of the hourglass). From there, users can apply basic and commonly used functionality to the loaded data, such as making selections, combining data, performing conversions, and carrying out slicing operations.

The results can then be exported in various formats and/or further used in different software and tools, depending on the task at hand.

At the core of the internal data structures are simple Pandas DataFrames, allowing more advanced users to go far beyond the standard set of methods offered by GeoST.

Current development strategy

GeoST is being developed incrementally based on project needs. This means that every time we require certain functionality within a project, we identify which part of this functionality is eligible for addition to GeoST. This part is then developed to the high coding, testing and documentation standards that we require for GeoST. This may take more resources at first, but will pay itself of as the developed functionality is reused in other projects.