GemPy: open-source stochastic geological modeling and inversion

Miguel Varga and Florian Wellmann and Alexander Schaaf. ( 2018 )
in: 2018 Ring Meeting

Abstract

The representation of subsurface structures is an essential aspect of a wide variety of geoscientific investigations and applications: ranging from geo uid reservoir studies, over raw material investigations, to geosequestration, as well as many branches of geoscientific research studies and applications in geological surveys. A wide range of methods exists to generate geological models. However, especially 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 GPU's. 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 analyse 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.

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BibTeX Reference

@INPROCEEDINGS{,
    author = { Varga de la, Miguel and Wellmann, Florian and Schaaf, Alexander },
     title = { GemPy: open-source stochastic geological modeling and inversion },
 booktitle = { 2018 Ring Meeting },
      year = { 2018 },
  abstract = { The representation of subsurface structures is an essential aspect of a wide variety of geoscientific investigations and applications: ranging from geo
uid reservoir studies, over raw material
investigations, to geosequestration, as well as many branches of geoscientific research studies and
applications in geological surveys. A wide range of methods exists to generate geological models.
However, especially 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 GPU's. 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 analyse 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. }
}