Research software

GIM advocates open research including the use and development of open-source software.

pyGIMLi

pyGIMLi is an open-source library for modelling and inversion in geophysics. The object-oriented library provides management for structured and unstructured meshes in 2D and 3D, finite-element and finite-volume solvers, various geophysical forward operators, as well as Gauss-Newton based frameworks for constrained, joint and fully-coupled inversions with flexible regularization.

What is pyGIMLi suited for?

  • analyze, visualize and invert geophysical data in a reproducible manner
  • forward modelling of (geo)physical problems on complex 2D and 3D geometries
  • inversion with flexible controls on a-priori information and regularization
  • combination of different methods in constrained, joint and fully-coupled inversions
  • teaching applied geophysics (e.g., in combination with Jupyter notebooks)

Reference

  • pyGIMLi: An open-source library for modelling and inversion in geophysics

    2017 | Rücker, C., Günther, T., Wagner, F. M.

    Computers & Geosciences, doi:10.1016/j.cageo.2017.07.011

    RWTH Publications PDF
    Note: This publication marks version 1.0 of pyGIMLi and resulted from collaboration with Carsten Rücker and Thomas Günther during Florian's time at GFZ Potsdam and the University of Bonn, i.e. was prepared before GIM was founded.

    Abstract

    Many tasks in applied geosciences cannot be solved by single measurements, but require the integration of geophysical, geotechnical and hydrological methods. Numerical simulation techniques are essential both for planning and interpretation, as well as for the process understanding of modern geophysical methods. These trends encourage open, simple, and modern software architectures aiming at a uniform interface for interdisciplinary and flexible modelling and inversion approaches. We present pyGIMLi (Python Library for Inversion and Modelling in Geophysics), an open-source framework that provides tools for modelling and inversion of various geophysical but also hydrological methods. The modelling component supplies discretization management and the numerical basis for finite-element and finite-volume solvers in 1D, 2D and 3D on arbitrarily structured meshes. The generalized inversion framework solves the minimization problem with a Gauss-Newton algorithm for any physical forward operator and provides opportunities for uncertainty and resolution analyses. More general requirements, such as flexible regularization strategies, time-lapse processing and different sorts of coupling individual methods are provided independently of the actual methods used. The usage of pyGIMLi is first demonstrated by solving the steady-state heat equation, followed by a demonstration of more complex capabilities for the combination of different geophysical data sets. A fully coupled hydrogeophysical inversion of electrical resistivity tomography (ERT) data of a simulated tracer experiment is presented that allows to directly reconstruct the underlying hydraulic conductivity distribution of the aquifer. Another example demonstrates the improvement of jointly inverting ERT and ultrasonic data with respect to saturation by a new approach that incorporates petrophysical relations in the inversion. Potential applications of the presented framework are manifold and include time-lapse, constrained, joint, and coupled inversions of various geophysical and hydrological data sets.

    Cite as

    Rücker, C. and Günther, T. and Wagner, F. M. (2017): pyGIMLi: An open-source library for modelling and inversion in geophysics. Computers & Geosciences. https://doi.org/10.1016/j.cageo.2017.07.011
pyGIMLi logo
Core developers
Additional resources

fpinv

fpinv is based on pyGIMLi and allows to invert geoelectrical and seismic refraction data for liquid water, ice, and air saturations as well as porosity with flexible means to include prior information on these parameters. Within the M.Sc. thesis of Johanna Klahold, the code has been extended along the time axis to enable time-lapse joint inversion of multiple geophysical monitoring data sets. In current research activities we are further developing the code to allow different geophysical methods and petrophysical relations to be included.

Additional resources

Reference

  • Quantitative imaging of water, ice and air in permafrost systems through petrophysical joint inversion of seismic refraction and electrical resistivity data

    2019 | Wagner, F. M., Mollaret, C., Günther, T., Kemna, A., Hauck, C.

    Geophysical Journal International, doi:10.1093/gji/ggz402

    RWTH Publications PDF
    Note: This publication resulted from Florian's time at the University of Bonn, i.e. was prepared before GIM was founded.

    Abstract

    Quantitative estimation of pore fractions filled with liquid water, ice and air is crucial for a process-based understanding of permafrost and its hazard potential upon climate-induced degradation. Geophysical methods offer opportunities to image distributions of permafrost constituents in a non-invasive manner. We present a method to jointly estimate the volumetric fractions of liquid water, ice, air and the rock matrix from seismic refraction and electrical resistivity data. Existing approaches rely on conventional inversions of both data sets and a suitable a priori estimate of the porosity distribution to transform velocity and resistivity models into estimates for the four-phase system, often leading to non-physical results. Based on two synthetic experiments and a field data set from an Alpine permafrost site (Schilthorn, Bernese Alps and Switzerland), it is demonstrated that the developed petrophysical joint inversion provides physically plausible solutions, even in the absence of prior porosity estimates. An assessment of the model covariance matrix for the coupled inverse problem reveals remaining petrophysical ambiguities, in particular between ice and rock matrix. Incorporation of petrophysical a priori information is demonstrated by penalizing ice occurrence within the first two meters of the subsurface where the measured borehole temperatures are positive. Joint inversion of the field data set reveals a shallow air-rich layer with high porosity on top of a lower-porosity subsurface with laterally varying ice and liquid water contents. Non-physical values (e.g. negative saturations) do not occur and estimated ice saturations of 0­50 per cent as well as liquid water saturations of 15­75 per cent are in agreement with the relatively warm borehole temperatures between −0.5  and 3 ° C. The presented method helps to improve quantification of water, ice and air from geophysical observations.

    Cite as

    Wagner, F. M. and Mollaret, C. and Günther, T. and Kemna, A. and Hauck, C. (2019): Quantitative imaging of water, ice and air in permafrost systems through petrophysical joint inversion of seismic refraction and electrical resistivity data. Geophysical Journal International. https://doi.org/10.1093/gji/ggz402