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https://github.com/florian-wagner/four-phase-inversion

Manuscript and code for joint inversion of seismic refraction and electrical resistivity data to estimate water, ice, and air contents
https://github.com/florian-wagner/four-phase-inversion

cryosphere geophysics inversion

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Manuscript and code for joint inversion of seismic refraction and electrical resistivity data to estimate water, ice, and air contents

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README 

          
## Imaging of water, ice, and air in permafrost systems through petrophysical joint inversion of seismic refraction and electrical resistivity data



by Florian Wagner, Coline Mollaret, Thomas Günther, Andreas Kemna, and Christian Hauck



---



[![DOI](https://img.shields.io/badge/DOI-10.1093/gji/ggz402-orange)](https://doi.org/10.1093/gji/ggz402)

[![License](https://img.shields.io/badge/license-BSD-green)](LICENSE.md)

[![powered by pyGIMLi](https://img.shields.io/badge/powered%20by-pyGIMLi-informational?style=flat&logo=python&logoColor=white)](https://www.pygimli.org)



**This repository contains the data and code to reproduce all results and figures published in the following paper:**



> Wagner, F. M., Mollaret, C., Günther, T., Kemna, A., & 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,  219, 1866-1875, [doi:10.1093/gji/ggz402](https://doi.org/10.1093/gji/ggz402).



![Workflow](schematic.svg)



*The code realizes both conventional and petrophysical joint inversion of seismic refraction and electrical resistivity data as schematically shown above.*



## 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, 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 0-50% as well as liquid water saturations

of 15-75% are in agreement with the relatively warm borehole temperatures

between -0.5 °C and 3 °C. The presented method helps to improve quantification

of water, ice, and air from geophysical observations.



## Structure of this repository



All source code used to generate the results and figures in the paper are in the

`code` folder. A Python library holds the important bits and pieces, which are

resued for calculations and figure generation run in Python scripts. The field

data used in this study are provided in the `data` folder and the sources for

the manuscript text and figures  (LaTeX) are in `manuscript`. See the

`README.md` files in each directory for a full description.



## Getting the code



You can download a copy of all the files in this repository by cloning the

[git](https://git-scm.com/) repository:



    git clone https://github.com/florian-wagner/four-phase-inversion.git



or [download a zip archive](https://github.com/florian-wagner/four-phase-inversion/archive/master.zip).



## Dependencies



You'll need a working Python environment on a Linux machine to run the code.

Other operating systems are generally possible, but have not been tested. The

recommended way to set up your environment is through the [Anaconda Python

distribution](https://www.anaconda.com/download/) which provides the `conda`

package manager. Anaconda can be installed in your user directory and does not

interfere with the system Python installation. The required dependencies are

specified in the file `environment.yml`.



We use `conda` virtual environments to manage the project dependencies in

isolation. Thus, you can install our dependencies without causing conflicts with

your setup (even with different Python versions).



Run the following command in the repository folder (where `environment.yml` is

located) to create a separate environment and install all required dependencies

in it:



    conda env create



## Reproducing the results



Before running any code you must activate the conda environment:



    conda activate four-phase-inversion



This will enable the environment for your current terminal session.

Any subsequent commands will use software that is installed in the environment.



To build the software, produce all results and figures, and compile

the manuscript PDF, run this in the top level of the repository:



    make all



If all goes well, the manuscript PDF will be placed in `manuscript/output`.



You can also run individual steps in the process using the `Makefile`s from the

`code` and `manuscript` folders. See the respective `README.md` files for

instructions.



## Quick example combining the steps above



    unset PYTHONPATH # to avoid conflicts with packages outside the conda env

    git clone https://github.com/florian-wagner/four-phase-inversion.git

    cd four-phase-inversion

    conda env create

    conda activate four-phase-inversion

    cd code

    make build

    make all



## License



All source code is made available under a BSD 3-clause license. You can freely

use and modify the code, without warranty, so long as you provide attribution

to the authors. See `LICENSE.md` for the full license text.



The manuscript itself is an Open Access article distributed under the terms of

the Creative Commons Attribution License

(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse,

distribution, and reproduction in any medium, provided the original work is

properly cited.



## Credits



The software implementation is based on [pyGIMLi](https://www.pygimli.org) (and

its dependencies), which would not exist without the dedication of Carsten

Rücker. This repository is heavily inspired by a [template for

reproducible research papers](https://www.leouieda.com/blog/paper-template.html)

by Leonardo Uieda.



## Note on branches

The `master` branch is compatible with the latest pyGIMLi version. In the `paper` branch, you will find the original version published with the above-mentioned paper.