Country

South Africa

Sector

Software Development

Project Aim
The project aims to improve geoscience and data modelling in 3D and to expand it for use by geological sciences other than geophysics & seismology.

Geoscience modelling and interpretation (PyGMI)

Python geoscience modelling and interpretation (PyGMI) is an open-source software development project, programmed in Python, originally intended to create potential field models. PyGMI also features a variety of interpretation tools to facilitate seismological processing. One of the aims of PyGMI is to provide a geoscientific tool for training, interpretation and modelling of geoscience data, free of charge to institutions locally and globally. This initiative gives small companies and universities access to software, which would otherwise have been very expensive. Clients and beneficiaries include the CGS, universities, the State, the mining industry and groundwater and environmental consultants, both locally and abroad.

The project aims to improve geoscience and data modelling in 3D and to expand it for use by geological sciences other than geophysics and seismology. The intention is to expand the utilities in PyGMI to provide capabilities for full geological modelling, as well as inversion capabilities for electromagnetic, gravity and magnetic data. The PyGMI project can support the CGS geoscience mapping programme by enabling scientists through innovative technologies at a low cost. The project is being tailor made to meet the needs of the CGS mapping programme, whether in the context of minerals, energy, hydrogeology or geohazard mapping.

Example of a model created with PyGMI.

Key Objectives:

  • Updating geophysical modelling techniques: addition of inversion or tools for geophysical modelling, including datasets other than magnetic and gravity, for example, magnetotelluric (MT) and electromagnetic (EM) data.
  • Expanding modelling to allow for the creation of 3D models using borehole data and other geological information, including standard geological profiles.
  • Expanding seismological and remote sensing tools, including change detection.
  • Expanding machine learning tools.
  • Adding modules to interpret seismic sections in 3D.