GemPy 1.0: open-source stochastic geological modeling and inversion

• Published in: Geoscientific Model Development 2019-01, Vol.12 (1), p.1-32
• Main Authors: de la Varga, Miguel, Schaaf, Alexander, Wellmann, Florian
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Bayesian analysis; Geological models; Geological research; Geological surveys; Geology; Gravitational fields; Gravity; Interactions; Interpolation; Inversions; Learning algorithms; Machine learning; Mathematical models; Methods; Modelling; Packages; Probability theory; Programming languages; Raw materials; Scientific research; Statistical inference; Stochasticity; Three dimensional models; Topology
• ISSN: 1991-9603; 1991-959X; 1991-962X; 1991-9603; 1991-962X
• DOI: 10.5194/gmd-12-1-2019

The representation of subsurface structures is an essential aspect of a wide variety of geoscientific investigations and applications, ranging from geofluid reservoir studies, over raw material investigations, to geosequestration, as well as many branches of geoscientific research and applications in geological surveys. A wide range of methods exist to generate geological models. However, the powerful methods are behind a paywall in expensive commercial packages. We present here a full open-source geomodeling method, based on an implicit potential-field interpolation approach. The interpolation algorithm is comparable to implementations in commercial packages and capable of constructing complex full 3-D geological models, including fault networks, fault–surface interactions, unconformities and dome structures. This algorithm is implemented in the programming language Python, making use of a highly efficient underlying library for efficient code generation (Theano) that enables a direct execution on GPUs. The functionality can be separated into the core aspects required to generate 3-D geological models and additional assets for advanced scientific investigations. These assets provide the full power behind our approach, as they enable the link to machine-learning and Bayesian inference frameworks and thus a path to stochastic geological modeling and inversions. In addition, we provide methods to analyze model topology and to compute gravity fields on the basis of the geological models and assigned density values. In summary, we provide a basis for open scientific research using geological models, with the aim to foster reproducible research in the field of geomodeling.