GebPy - a Python-based, open source tool for the generation of synthetic geophysical and geochemical data of minerals, rocks and whole sequences

GebPy is an open-source, Python-based tool developed to simulate synthetic geophysical and geochemical properties of minerals, rocks and entire rock sequences. It is based on the concept that the properties of a rock are fundamentally determined by its mineralogical composition and its interaction with fluids like water. By adopting a bottom-up approach, GebPy systematically builds from the mineral scale to the rock and sequence level, providing a robust framework for understanding and modeling geophysical and geochemical systems.

The modeling process begins at the mineral level, where properties such as density, volume, seismic velocity or gamma radiation are calculated based on chemical composition and fundamental physical and crystallographic principles. These properties are then combined to simulate the bulk properties of a rock, accounting for the proportional contributions of individual minerals. GebPy also integrates the effects of pore fluids, such as water, to provide a realistic representation of subsurface conditions.

One of the key strengths of GebPy is its scalability. Beyond individual minerals and rocks, the tool enables the simulation of entire rock sequences, generating idealized datasets that are highly valuable for geophysical applications. These include seismic modeling, well-log analysis and the evaluation of geophysical inversion techniques. This ability to model idealized systems provides insights into the fundamental principles governing geophysical and geochemical behavior, making GebPy a powerful resource for both theoretical and applied research.

The development of GebPy was inspired once by the study of well-log diagrams, which play a critical role in resource exploration. By generating synthetic well-log data under idealized conditions - free from natural imperfections such as fractures or measuring artifacts - GebPy provides a new perspective on how these diagrams might look in a perfect geological scenario. This approach not only enhances our understanding of the fundamental properties of rocks but also supports the development of advanced modeling techniques and the evaluation of uncertainty sources.

Looking ahead, GebPy’s development roadmap includes the integration of advanced techniques such as machine learning and the use of experimental data for calibrating input parameters like elastic properties of minerals. These enhancements will further expand its capabilities, enabling even more precise and comprehensive simulations. GebPy’s open-source nature ensures that it can be continuously improved and adapted by the geoscientific community, fostering collaboration and innovation.

GebPy represents an important step forward in the generation of synthetic geophysical and geochemical data. By providing a scalable and reliable framework for modeling minerals, rocks and sequences, it empowers researchers and practitioners to explore geological systems with precision and flexibility.