GeoPython 2021

3D Geological Modelling using GemPy
2021-04-22, 18:00–18:30, Track 2

Several 3D modelling and visualization Python scripts have been combined into a seamless workflow and subsequently applied to a sparse dataset in a geologically complex area. Preliminary 3D-model results are encouraging and align with known and inferred regional geology.

The geological field experience is traditionally directed on raw data collection with orientation measurements, observations and rock samples descriptions representing some classic examples. Creating geologic and contour maps by hand are also prominent activities within the limited timeframe.
We aim to improve this strategy by introducing a seamless and iterative 3D-modelling workflow, in the pursuit of shifting focus back to geological idea and concept integration versus data. Our proposed workflow is intended to work in-parallel, thereby bolstering the efficiency of allotted field time.
The workflow for 3D-modelling and visualization combines new and existing Python scripts and using open-source tools to furnish users with a coherent approach for achieving both maps and models. Our approach utilizes GemPy, a 3D geological structural modelling tool, based on the Potential Field (PF) method. Using a sparse dataset, a regional 3D-model was generated and also easily re-generated upon the introduction of new data. Cross-section views of the 3D-model can also be obtained and 2D geological maps may be extracted.
With respect to data management, well-known tools such as rasterio, geopandas and numpy were exploited for data imports and processing. The digital elevation model (DEM), field data stored as shapefiles and other required data were organized into a single geopackage, which can be shared and updated as needed. Much effort has been placed on the ease-of-use for data organization.
Our proposed approach may have strong impacts on field data collection and decisions, especially in regions with sparse geological knowledge. This notion is supported by promising initial results, well-aligned with inferred regional geology.