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CITATION.cff
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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: [email protected]
orcid: 'https://orcid.org/0000-0002-8231-9861'
- given-names: Thomas
family-names: Günther
email: [email protected]
orcid: 'https://orcid.org/0000-0001-5409-0273'
- given-names: Florian
family-names: Wagner
email: [email protected]
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: [email protected]
orcid: 'https://orcid.org/0000-0002-8231-9861'
- given-names: Thomas
family-names: Günther
email: [email protected]
orcid: 'https://orcid.org/0000-0001-5409-0273'
- given-names: Florian M.
family-names: Wagner
email: [email protected]
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