Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2018Interpretation of WISDOM/ExoMars 2020 data acquired on natural dry environmentscitations
  • 2016Plasma properties at 67P/Churyumov-Gerasimenko: a comparision between PP-SESAME/Philae/Rosetta and RPC/MIP/Rosettacitations

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Chart of shared publication
Benedix, Wolf-Stefan
1 / 4 shared
Loizeau, Damien
1 / 1 shared
Herve, Yann
1 / 6 shared
Plettemeier, Dirk
1 / 20 shared
Oudart, Nicolas
1 / 9 shared
Le Gall, Alice
1 / 16 shared
Hamelin, Michel
1 / 13 shared
Gall, Alice Le
1 / 3 shared
Henri, Pierre
1 / 4 shared
Lebreton, Jean Pierre
1 / 1 shared
Fischer, Hans-Herbert
1 / 3 shared
Lethuillier, Anthony
1 / 9 shared
Grard, Réjean
1 / 6 shared
Seidensticker, Klaus
1 / 1 shared
Caujolle-Bert, Sylvain
1 / 6 shared
Schmidt, Walter
1 / 4 shared
Vallières, Xavier
1 / 3 shared
Chart of publication period
2018
2016

Co-Authors (by relevance)

  • Benedix, Wolf-Stefan
  • Loizeau, Damien
  • Herve, Yann
  • Plettemeier, Dirk
  • Oudart, Nicolas
  • Le Gall, Alice
  • Hamelin, Michel
  • Gall, Alice Le
  • Henri, Pierre
  • Lebreton, Jean Pierre
  • Fischer, Hans-Herbert
  • Lethuillier, Anthony
  • Grard, Réjean
  • Seidensticker, Klaus
  • Caujolle-Bert, Sylvain
  • Schmidt, Walter
  • Vallières, Xavier
OrganizationsLocationPeople

document

Interpretation of WISDOM/ExoMars 2020 data acquired on natural dry environments

  • Ciarletti, Valerie
  • Benedix, Wolf-Stefan
  • Loizeau, Damien
  • Herve, Yann
  • Plettemeier, Dirk
  • Oudart, Nicolas
  • Le Gall, Alice
Abstract

The rover of the future ExoMars 2020 mission to Mars has on board a polarimetric ground penetrating radar named WISDOM (Water Ice Subsurface Deposits Observation on Mars) that will sound and characterize the subsurface along the rover path. WISDOM will provide data that are essential to the understanding of the local geological context and to the selection of the most promising locations to collect samples. Thanks to its broad frequency bandwidth of 2.5 GHz, WISDOM is able to probe down to a depth of approximately 3 m in lithic environments with a vertical resolution of a few centimeters that is requested for the drilling operations. The WISDOM team is currently working on the preparation of the scientific return of the WISDOM data that will be collected on Mars. In this paper, we specifically focus on the tools that are developed to automatically detect and characterize potential rocks buried in the subsurface, and extract all the possible information from their radar signatures. We show that constraints on the composition of the subsurface can be found through the retrieval of its permittivity and that the geological context of the site can be inferred from the shape and the density of rocks in the subsurface (as they can be related to the geological processes that have shaped and placed them there, such as impacts, fluvial processes, volcanism, … ). The algorithm we developed is based on neural network that is used to detect areas in the subsurface that show presence of rocks/blocs. It is then followed by a Hough transform that is used to analyze each radar signature to get an estimate of the local permittivity value (which is mandatory to convert the measured delays in accurate distances). The algorithm has been tested and validated on simulated data obtained with a 3D FDTD code. It has been designed to be run on the data that will be collected on Mars in the limited time allocated during the mission's operations. We present results obtained by this automatic detection of structures of interest on experimental data acquired in a semi-controlled environment and on Martian analogues (South of France and Atacama Desert), that demonstrate the ability of WISDOM to provide clues about the geological context of a site....

Topics
  • density
  • impedance spectroscopy