cff-version: 1.2.0
title: >-
  pyGIMLi: An open-source library for modelling and
  inversion in geophysics
message: >-
  If you use this software, please cite it using the metadata from
  'preferred-citation'.
type: software
authors:
  - given-names: Carsten
    family-names: Rücker
    email: carsten@pygimli.org
    orcid: 'https://orcid.org/0000-0002-8231-9861'
  - given-names: Thomas
    family-names: Günther
    email: thomas@pygimli.org
    orcid: 'https://orcid.org/0000-0001-5409-0273'
  - given-names: Florian
    family-names: Wagner
    email: florian@pygimli.org
    orcid: 'https://orcid.org/0000-0001-7407-9741'
repository-code: 'https://github.com/gimli-org/gimli'
url: 'https://www.pygimli.org'
license: Apache-2.0

preferred-citation:
  type: article
  journal: Computers and Geosciences
  year: 2017
  title: >-
    pyGIMLi: An open-source library for modelling and
    inversion in geophysics
  authors:
    - given-names: Carsten
      family-names: Rücker
      email: carsten@pygimli.org
      orcid: 'https://orcid.org/0000-0002-8231-9861'
    - given-names: Thomas
      family-names: Günther
      email: thomas@pygimli.org
      orcid: 'https://orcid.org/0000-0001-5409-0273'
    - given-names: Florian M.
      family-names: Wagner
      email: florian@pygimli.org
      orcid: 'https://orcid.org/0000-0001-7407-9741'
  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.
  doi: 10.1016/j.cageo.2017.07.011 
  volume: 109
  start: 106
  end: 123
  license: CC-BY-4.0