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Automatic stochastic 3D clay fraction model from tTEM survey and borehole data

In most urbanized and agricultural areas of central Europe, the shallow underground is constituted of Quaternary deposits which are often the most extensively used layers (water pumping, shallow geothermic, material excavation). All these deposits are often complexly intertwined, leading to high spa...

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Autores principales: Neven, Alexis, Christiansen, Anders Vest, Renard, Philippe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9556523/
https://www.ncbi.nlm.nih.gov/pubmed/36224281
http://dx.doi.org/10.1038/s41598-022-21555-z
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author Neven, Alexis
Christiansen, Anders Vest
Renard, Philippe
author_facet Neven, Alexis
Christiansen, Anders Vest
Renard, Philippe
author_sort Neven, Alexis
collection PubMed
description In most urbanized and agricultural areas of central Europe, the shallow underground is constituted of Quaternary deposits which are often the most extensively used layers (water pumping, shallow geothermic, material excavation). All these deposits are often complexly intertwined, leading to high spatial variability and high complexity. Geophysical data can be a fast and reliable source of information about the underground. Still, the integration of these data can be time-consuming, it lacks realistic interpolation in a full 3D space, and the final uncertainty is often not represented. In this study, we propose a new methodology to combine boreholes and geophysical data with uncertainty in an automatic framework. A spatially varying translator function that predicts the clay fraction from resistivity is inverted using boreholes description as control points. It is combined with a 3D stochastic interpolation framework based on a Multiple Points Statistics algorithm and Gaussian Random Function. This novel workflow allows incorporating robustly the data and their uncertainty and requires less user intervention than the already existing workflows. The methodology is illustrated for ground-based towed transient electromagnetic data (tTEM) and borehole data from the upper Aare valley, Switzerland. In this location, a 3D realistic high spatial resolution model of clay fraction was obtained over the whole valley. The very dense data set allowed to demonstrate the quality of the predicted values and their corresponding uncertainties using cross-validation.
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spelling pubmed-95565232022-10-14 Automatic stochastic 3D clay fraction model from tTEM survey and borehole data Neven, Alexis Christiansen, Anders Vest Renard, Philippe Sci Rep Article In most urbanized and agricultural areas of central Europe, the shallow underground is constituted of Quaternary deposits which are often the most extensively used layers (water pumping, shallow geothermic, material excavation). All these deposits are often complexly intertwined, leading to high spatial variability and high complexity. Geophysical data can be a fast and reliable source of information about the underground. Still, the integration of these data can be time-consuming, it lacks realistic interpolation in a full 3D space, and the final uncertainty is often not represented. In this study, we propose a new methodology to combine boreholes and geophysical data with uncertainty in an automatic framework. A spatially varying translator function that predicts the clay fraction from resistivity is inverted using boreholes description as control points. It is combined with a 3D stochastic interpolation framework based on a Multiple Points Statistics algorithm and Gaussian Random Function. This novel workflow allows incorporating robustly the data and their uncertainty and requires less user intervention than the already existing workflows. The methodology is illustrated for ground-based towed transient electromagnetic data (tTEM) and borehole data from the upper Aare valley, Switzerland. In this location, a 3D realistic high spatial resolution model of clay fraction was obtained over the whole valley. The very dense data set allowed to demonstrate the quality of the predicted values and their corresponding uncertainties using cross-validation. Nature Publishing Group UK 2022-10-12 /pmc/articles/PMC9556523/ /pubmed/36224281 http://dx.doi.org/10.1038/s41598-022-21555-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Neven, Alexis
Christiansen, Anders Vest
Renard, Philippe
Automatic stochastic 3D clay fraction model from tTEM survey and borehole data
title Automatic stochastic 3D clay fraction model from tTEM survey and borehole data
title_full Automatic stochastic 3D clay fraction model from tTEM survey and borehole data
title_fullStr Automatic stochastic 3D clay fraction model from tTEM survey and borehole data
title_full_unstemmed Automatic stochastic 3D clay fraction model from tTEM survey and borehole data
title_short Automatic stochastic 3D clay fraction model from tTEM survey and borehole data
title_sort automatic stochastic 3d clay fraction model from ttem survey and borehole data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9556523/
https://www.ncbi.nlm.nih.gov/pubmed/36224281
http://dx.doi.org/10.1038/s41598-022-21555-z
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