pyGIMLi is an open-source library for multi-method modelling and inversion in geophysics.

Showcase pyGIMLi-based research papers

Inference analysis of model ensembles

Arboleda-Zapata et al. (2022), Journal of Applied Geophysics

Coupling electrical and magnetic resonance soundings

Skibbe et al. (2018), Geophysics

Mapping bedrock topography

Uecker et al. (2022), Frontiers in Water

Testing conceptual models in hydrogeology using remote sensing and geophysics

Enemark et al. (2020), Water Resources Research

Cole-Cole and Debye fitting of SIP/DP spectroscopy

Loewer et al. (2017), Geophysical Journal International

Direct-push-constrained ERT inversion

Grünenbaum et al. (2023), Journal of Hydrology

Spectrally constrained inversion of induced polarization data

Günther & Martin (2016), Journal of Applied Geophysics

Complex EM modelling using the open-source code custEM

Rochlitz et al. (2019), Geophysics

Optimized survey design for storage reservoir monitoring

Wagner et al. (2015), Geophysics

Open-source 2D magnetic resonance tomography

Skibbe et al. (2020), Geophysics

3D CSEM inversion

Rochlitz et al. (2023), Geophysical Journal International

Structural joint inversion with cross-gradients on irregular meshes

Jordi et al. (2020), Geophysical Journal International

Augmented reality sandbox for education and outreach

Wellmann et al. (2022), Geosphere

Structural constraints on ERT derived from reflection seismics

Bergmann et al. (2014), Geophysics

Improved geophysical imaging through geostatistical regularization

Jordi et al. (2018), Geophysical Journal International

Coupling underwater electrial resistivity and seismic refraction tomography

Ronczka et al. (2017), Solid Earth

Structurally coupled inversion of ERT and seismic refraction

Hellman et al. (2017), Journal of Applied Geophysics

Monitoring seawater intrusion dynamics with crosshole ERT

Palacios et al. (2020), Hydrol. Earth Syst. Sci.

DC/IP imaging of trees

Martin & Günther (2013), European Journal of Forest Research

Simulating the electrical response of seawater intrusion in Australia

Costall et al. (2020), Scientific Reports

Petrophysical joint inversion for water, ice, and air

Wagner et al. (2019), Geophysical Journal International

Block joint electrical and resonance sounding inversion

Günther & Müller-Petke (2012), Hydrol. Earth Syst. Sci.

Improved imaging using parameter and structure constraints

Wunderlich et al. (2018), Geophysics

Crosshole GPR with deep neural networks

Laloy et al. (2020)

Probabilistic inference of subsurface heterogeneity and interface geometry

de Pasquale et al. (2019), Geophysical Journal International

Structurally coupled MRT and ERT

Skibbe et al. (2021), Geophysics

Imaging electrified plant roots

Peruzzo et al. (2020), Plant and Soil

P-wave refraction imaging of the critical zone

Flinchum et al. (2022), Frontiers in Water

3D SAEM survey design

Nazari et al. (2023), Minerals

Laterally-constrained inversion of airborne MRS

Costabel et al. (2016), Geophysics

Long-electrode ERT modelling study

Ronczka et al. (2015), Geophysics

Ice estimation in alpine permafrost via joint inversion

Mollaret et al. (2020), Front. Earth Sci.

Aquifer monitoring with long electrode ERT

Ronczka et al. (2015), Journal of Applied Geophysics

3D ERT monitoring of infiltration processes in a hillslope

Hübner et al. (2017), Hydrol. Earth Syst. Sci.

Get pyGIMLi. Available for different platforms.

pyGIMLi works on Windows, various Linux distributions and Mac OS. See the installation section for more details on how to install the binaries or build pyGIMLi from the source code.

Get started. Check out the tutorials.

To get started, have a look at the pyGIMLi tutorials. Applications and user stories can be found in the Example section.

Get involved. We want your help.

You found a bug or desperately miss a certain functionality? Fill out a bug report or a feature request. If you have used pyGIMLi for an interesting application, consider sending us your example. Please cite the pyGIMLi paper or derived ones when using it for scientific purposes.