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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2018-335
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2018-335
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 20 Mar 2019

Model description paper | 20 Mar 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

CobWeb 1.0: Machine Learning Tool Box for Tomographic Imaging

Swarup Chauhan1, Kathleen Sell1,3, Freider Enzmann1, Wolfram Rühaak4, Thorsten Wille5, Ingo Sass2, and Michael Kersten1 Swarup Chauhan et al.
  • 1Institute for Geosciences, Johannes Gutenberg-University, Mainz 55099, Germany
  • 2Institute of Applied Geosciences, University of Technology, Darmstadt 64287, Germany
  • 3igem – Institute for Geothermal Resource Management, Berlinstr. 107a, Bingen 55411, Germany
  • 4Federal Institute for Geosciences and Natural Resources (BGR), Hannover 30655, Germany
  • 5APS Antriebs-, Prüf-und Steuertechnik GmbH, Götzenbreite 12, Göttingen-Rosdorf 37124, Germany

Abstract. Despite the availability of both commercial and open source software, an ideal tool for digital rock physics analysis for accurate automatic image analysis at ambient computational performance is difficult to pin point. More often image segmentation is driven manually where the performance remains limited to two phases. Discrepancies due to artefacts causes inaccuracies in image analysis. To overcome these problems, we have developed CobWeb 1.0 which is automated and explicitly tailored for accurate grayscale (multi-phase) image segmentation using unsupervised and supervised machine learning techniques. The simple and intuitive layout of the graphical user interface enables easy access to perform Image enhancement, Image segmentation and further to obtain the accuracy of different segmented classes. The graphical user interface enables not only processing of a full 3D digital rock dataset but also provides a quick and easy region-of-interest selection, where a representative elementary volume can be extracted and processed. The CobWeb software package covers image processing and machine learning libraries of MATLAB® used for image enhancement and image segmentation operations, which are compiled into series of windows executable binaries. Segmentation can be performed using unsupervised, supervised and ensemble classification tools. Additionally, based on the segmented phases, geometrical parameters such as pore size distribution, relative porosity trends and volume fraction can be calculated and visualized. The CobWeb software allows the export of data to various formats such as ParaView (.vtk), DSI Studio (.fib) for visualization and animation and Microsoft® Excel and MATLAB® for numerical calculation and simulations. The capability of this new software is verified using high-resolution synchrotron tomography datasets, as well as lab-based (cone-beam) X-ray micro-tomography datasets. Albeit the high spatial resolution (sub-micrometer), the synchrotron dataset contained edge enhancement artefacts which were eliminated using a novel dual filtering and dual segmentation procedure.

Swarup Chauhan et al.
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Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Swarup Chauhan et al.
Data sets

Synchrotron tomography dataset of Gas Hydrate S. Chauhan, K. Sell, Kathleen, F. Enzmann, W. Rühaak, T. Wille, I. Sass, and M. Kersten https://doi.org/10.5281/zenodo.2390943

Model code and software

CobWeb 1.0: machine learning tool box for tomographic imaging S. Chauhan, K. Sell, Kathleen, F. Enzmann, W. Rühaak, T. Wille, I. Sass, and M. Kersten https://doi.org/10.5281/zenodo.2390943

Video supplement

CobWeb 1.0 Demo S. Chauhan, K. Sell, Kathleen, F. Enzmann, W. Rühaak, T. Wille, I. Sass, and M. Kersten https://doi.org/10.5281/zenodo.2390943

Swarup Chauhan et al.
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Latest update: 18 Jun 2019
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Short summary
We present CobWeb 1.0, a graphical user interface for analysing tomographic images of geomaterials. CobWeb offers different machine learning techniques for accurate multiphase image segmentation and visualizing material specific parameters such as pore size distribution, relative porosity & volume fraction. Further, we demonstrate a novel approach of dual filtration & dual segmentation to eliminate edge enhancement artefact in synchrotron-tomographic datasets and provide the computational code.
We present CobWeb 1.0, a graphical user interface for analysing tomographic images of...
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