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    The Rochelec dataset gathers petrophysical and geoelectrical data from the Rochechouart impact structure (France). Since 2017, about 10 geophysical field campaigns were performed on this eroded structure. Among other techniques, geophysical downhole logging, electrical resistivity tomography and controlled-source audiomagnetotelluric data were acquired. In parallel, we measured the electrical resistivity and porosity of some core samples coming from drillings performed in fall 2017 at several sites of the impact structure. These multiscale electric data allows to better characterize the different lithologies outside the drilling sites, and their associated geometry. * Citation of this dataset Quesnel, Y., Sailhac, P., Lofi, J., Lambert, P., Rochette, P., Uehara, M. & Camerlynck, C. (2021). RochElec : Geoelectrical investigations on the Rochechouart impact structure (France) [Data set]. CEREGE UMR 7330 CNRS. https://doi.org/10.34930/BE0549D1-E876-49C5-B07F-BF04D398B25E * Publications linked with the dataset: Quesnel, Y., Sailhac, P., Lofi, J., Lambert, P., Rochette, P., Uehara, M. & Camerlynck, C. (2021). Rochechouart impact structure, France. Geochemistry, Geophysics, Geosystems, 22, e2021GC010036, https://doi.org/10.1029/2021GC010036

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    The GeoKarla dataset gathers geophysical and petrophysical data acquired over the Karla impact structure (Tatarstan, Russia). In September 2019, a field campaign on this eroded and buried structure was performed. Magnetic and gravity field observations were done, as well as geological mapping and sampling. Further petrophysical analyses in laboratory were performed on rock samples. All these data reveal - for the first time - a clear but unusual geophysical signature of the Karla impact structure. ==== acknowledgements ====== The associated research project was funded by: Russian Foundation for Basic Research RFBR grant no.18-55-5014 CNRS PRC French program Institutes/Participants: * Aix-Marseille Université, CNRS, IRD, INRAE, Aix-en-Provence, France Quesnel, Y., Rochette, P., Gattacceca, J., Uehara, M. * Institute of Geology and Petroleum Technologies, Kazan Federal University, 4/5 Kremlyovskaya Str., 420008, Kazan, Russia Bezaeva, N.S., Kuzina, D.M., Nasyrtdinov, B.M. * V.I. Vernadsky Institute of Geochemistry and Analytical Chemistry, Russian Academy of Scences, 19 Kosygin str., 119991 Moscow, Russia Bezaeva, N.S. , Badyukov, D.D. * Institute of Physics and Technology, Ural Federal University, 19 Mira Str., 620002 Ekaterinburg, Russia Bezaeva, N.S. Chareev, D.A. * Institute Experimental Mineralogy, Russian Academy of Science, 4 Academician Osipyan Str., 142432 Chernogolovka, Moscow Region, Russia Chareev, D.A. * National University of Science and Technology “MISiS”, 4 Leninsky Prospekt, 119049 Moscow, Russia Chareev, D.A. * Université de Montpellier, CNRS, Géosciences Montpellier, France Champollion, C.

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    AcoustRivNN proposes to develop a system to estimate the flow and the granulometry of the sediment transport in a river from the acoustic pressure generated by the latter using methods from artificial intelligence. The estimation of the sediment flow carried by water, in rivers or estuaries, is a crucial issue for the management of the latter, allowing to carry out scientific studies, restoration or prevention projects, as well as operational works. Given the lack of effective methods to estimate the flow of sediment, the AcoustRivNN project proposes to provide a "proof of concept" by developing an original system based on deep learning to estimate the flow of coarse sediments from the simple acoustic pressure generated by the latter and measured by hydrophones. The originality of this project lies, in particular, in its interdisciplinary aspect proposing to adapt methods from artificial intelligence particularly effective in many applied fields. This project, which is part of the transversal axis 2: "Observations/information systems/modeling" of the ECCOREV federation, is structured in two phases: - Phase I proposes to build an acoustic database referenced in the laboratory necessary for the training of a neural network. - Phase II aims to develop a neural network model to characterize the acoustics of sediment flow. DOI: https://doi.org/10.34930/dc3225de-ef03-4134-927e-2347d75d8b41 Citation: Gassier, G., Michal, T., & Dussouillez, P. (2022). AcoustRivNN : flow and the granulometry of the sediment transport in a river from acoustic pressure [Data set]. CEREGE UMR 7330 CNRS. https://doi.org/10.34930/DC3225DE-EF03-4134-927E-2347D75D8B41