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

Oussama Jabrane; Driss El Azzab; Pedro Martínez-Pagán; Marcos A Martínez-Segura; Himi Mahjoub; Mohammed Charroud

doi:10.1016/j.dib.2022.108763

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

Data Brief. 2022 Nov 20:46:108763. doi: 10.1016/j.dib.2022.108763. eCollection 2023 Feb.

Authors

Oussama Jabrane¹, Driss El Azzab¹, Pedro Martínez-Pagán², Marcos A Martínez-Segura², Himi Mahjoub³, Mohammed Charroud¹

Affiliations

¹ Sidi Mohamed Ben Abdellah University, Faculty of Science and Technology, SIGER Laboratory, Fez, BP2202, Morocco.
² Department of Mining and Civil Engineering, Universidad Politecnica de Cartagena, Paseo Alfonso XIII, 52, 30203, Cartagena, Spain.
³ Department of Petrology, Geochemistry and Geological Prospection, Universitat de Barcelona, Martí i Franquès s/n, 08030 Barcelona, Spain.

Abstract

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.

Keywords: BERT software; ERT inversion algorithms; Electrical resistivity imaging data; Fez; Landslide occurrence; pyGIMLi software.