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Contribution of Python-based BERT software for landslide monitoring using Electrical Resistivity Tomography datasets. A case study in Tghat-Fez (Morocco)

The electrical resistivity tomography (ERT) technique was conducted for the geophysical survey of a landslide on the southern slope of Jbel Tghat, north of the city of Fez, Morocco. Nine electrical resistivity tomography profiles were implemented to: (a) characterize the geometry of the dipping zone...

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Autores principales: Jabrane, Oussama, Azzab, Driss El, Martínez-Pagán, Pedro, Martínez-Segura, Marcos A., Mahjoub, Himi, Charroud, Mohammed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9720517/
https://www.ncbi.nlm.nih.gov/pubmed/36478686
http://dx.doi.org/10.1016/j.dib.2022.108763
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author Jabrane, Oussama
Azzab, Driss El
Martínez-Pagán, Pedro
Martínez-Segura, Marcos A.
Mahjoub, Himi
Charroud, Mohammed
author_facet Jabrane, Oussama
Azzab, Driss El
Martínez-Pagán, Pedro
Martínez-Segura, Marcos A.
Mahjoub, Himi
Charroud, Mohammed
author_sort Jabrane, Oussama
collection PubMed
description The electrical resistivity tomography (ERT) technique was conducted for the geophysical survey of a landslide on the southern slope of Jbel Tghat, north of the city of Fez, Morocco. Nine electrical resistivity tomography profiles were implemented to: (a) characterize the geometry of the dipping zone; (b) characterize their internal structures; and (c) highlight the faulting zone between the marly deposits and the conglomerate formation. The measured data sets were processed using EarthImager™ 2D (Advanced Geosciences, Inc), and BERT (Boundless Electrical Resistivity Tomography) software packages that offer a simple workflow from data import to inversion and visualization, while offering full control over inversion parameters. Moreover, BERT software is a Python-based open-source inversion software package. Both ERT processing software allows obtaining 2D subsurface electrical models associated with the distribution of the subsurface apparent electrical resistivity property, in Ohm.m units. Those 2D subsurface electrical models are retrieved using the same inversion parameters to determine the distribution of geoelectric layers and their defining parameters (e.g., electrical resistivity, thickness, and depth), giving access to certain characteristics exclusive to one of the two processing techniques, comparing the inversion findings to better understand the process's limits, as well as evaluating the capabilities of the two inversion methods.
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spelling pubmed-97205172022-12-06 Contribution of Python-based BERT software for landslide monitoring using Electrical Resistivity Tomography datasets. A case study in Tghat-Fez (Morocco) Jabrane, Oussama Azzab, Driss El Martínez-Pagán, Pedro Martínez-Segura, Marcos A. Mahjoub, Himi Charroud, Mohammed Data Brief Data Article The electrical resistivity tomography (ERT) technique was conducted for the geophysical survey of a landslide on the southern slope of Jbel Tghat, north of the city of Fez, Morocco. Nine electrical resistivity tomography profiles were implemented to: (a) characterize the geometry of the dipping zone; (b) characterize their internal structures; and (c) highlight the faulting zone between the marly deposits and the conglomerate formation. The measured data sets were processed using EarthImager™ 2D (Advanced Geosciences, Inc), and BERT (Boundless Electrical Resistivity Tomography) software packages that offer a simple workflow from data import to inversion and visualization, while offering full control over inversion parameters. Moreover, BERT software is a Python-based open-source inversion software package. Both ERT processing software allows obtaining 2D subsurface electrical models associated with the distribution of the subsurface apparent electrical resistivity property, in Ohm.m units. Those 2D subsurface electrical models are retrieved using the same inversion parameters to determine the distribution of geoelectric layers and their defining parameters (e.g., electrical resistivity, thickness, and depth), giving access to certain characteristics exclusive to one of the two processing techniques, comparing the inversion findings to better understand the process's limits, as well as evaluating the capabilities of the two inversion methods. Elsevier 2022-11-20 /pmc/articles/PMC9720517/ /pubmed/36478686 http://dx.doi.org/10.1016/j.dib.2022.108763 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Jabrane, Oussama
Azzab, Driss El
Martínez-Pagán, Pedro
Martínez-Segura, Marcos A.
Mahjoub, Himi
Charroud, Mohammed
Contribution of Python-based BERT software for landslide monitoring using Electrical Resistivity Tomography datasets. A case study in Tghat-Fez (Morocco)
title Contribution of Python-based BERT software for landslide monitoring using Electrical Resistivity Tomography datasets. A case study in Tghat-Fez (Morocco)
title_full Contribution of Python-based BERT software for landslide monitoring using Electrical Resistivity Tomography datasets. A case study in Tghat-Fez (Morocco)
title_fullStr Contribution of Python-based BERT software for landslide monitoring using Electrical Resistivity Tomography datasets. A case study in Tghat-Fez (Morocco)
title_full_unstemmed Contribution of Python-based BERT software for landslide monitoring using Electrical Resistivity Tomography datasets. A case study in Tghat-Fez (Morocco)
title_short Contribution of Python-based BERT software for landslide monitoring using Electrical Resistivity Tomography datasets. A case study in Tghat-Fez (Morocco)
title_sort contribution of python-based bert software for landslide monitoring using electrical resistivity tomography datasets. a case study in tghat-fez (morocco)
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9720517/
https://www.ncbi.nlm.nih.gov/pubmed/36478686
http://dx.doi.org/10.1016/j.dib.2022.108763
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