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; (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.

Electrical resistivity tomography (ERT) and geochemical analysis dataset to delimit subsurface affected areas by livestock pig slurry ponds.

The electrical resistivity tomography (ERT) technique was employed with the support of geochemical analyses to delimit the affected surface area by slurry pig ponds. Data were taken in three selected slurry ponds located in Fuente Álamo municipality, Murcia region (SE Spain), to obtain electrical resistivity value-based 2D sections and 3D blocks. All ERT-based survey data were obtained in September 2020 using a SuperSting R8 resistivity meter from Advanced Geosciences Inc. and using the dipole-dipole array consisting of a total of twenty-eight electrodes. The soil samples were taken from drilling core sampling by boreholes at each slurry pond, and physical-chemical analyses of soil samples were obtained using standard laboratory testing methods. Electrical resistivity values and physical-chemical analysis data obtained from soil samples were contrasted, whose comparison showed a correlation between profiles-based electrical resistivity, laboratory-based electrical conductivity (EC) data, and nitrate (N-NO3-) content from soil samples. The statistical analysis was run by SPSS Statistics v.23 software (IBM, Neconductivity York, NY, USA) to establish the non-parametric Spearman correlation. The dataset establishes a reliable methodology and provides insight and information to delimit the affected subsurface area by pig slurry. Data contained within this publication are presented concurrently with Capa-Camacho et al. 2022 [1].

Soil Water Content Prediction Using Electrical Resistivity Tomography (ERT) in Mediterranean Tree Orchard Soils.

Water scarcity in arid and semiarid regions poses problems for agricultural systems, awakening special interest in the development of deficit irrigation strategies to improve water conservation. Toward this purpose, farmers and technicians must monitor soil water and soluble nutrient contents in real time using simple, rapid and economical techniques through time and space. Thus, this study aimed to achieve the following: (i) create a model that predicts water and soluble nutrient contents in soil profiles using electrical resistivity tomography (ERT); and (ii) apply the model to different woody crops under different irrigation regimes (full irrigation and regulated deficit irrigation (RDI)) to assess the efficiency of the model. Simple nonlinear regression analysis was carried out on water content and on different ion contents using electrical resistivity data as the dependent variable. A predictive model for soil water content was calibrated and validated with the datasets based on exponential decay of a three-parameter equation. Nonetheless, no accurate model was achieved to predict any soluble nutrient. Electrical resistivity images were replaced by soil water images after application of the predictive model for all studied crops. They showed that under RDI situations, soil profiles became drier at depth while plant roots seemed to uptake more water, contributing to reductions in soil water content by the creation of desiccation bulbs. Therefore, the use of ERT combined with application of the validated predictive model could be a sustainable strategy to monitor soil water evolution in soil profiles under irrigated fields, facilitating land irrigation management.

Assessing the behaviour of heavy metals in abandoned phosphogypsum deposits combining electrical resistivity tomography and multivariate analysis.

This study presents the results of a physical-chemical characterisation of phosphogypsum deposits generated with hydrochloric and sulphuric acid during the wet acid process. The paper aims to establish an efficient methodology based on electrical resistivity tomography (ERT), chemical analysis and multivariate analysis identifying the areas most contaminated by heavy metals in an abandoned factory where fertiliser was derived from phosphoric rock. This fertiliser has provided many benefits to agriculture; however, it generates a vast amount of waste (5 tonnes phosphoric rock/1 tonne fertiliser). The chemical composition of this by-product varies according to the industrial process performed. Hydrochloric acid (HCl) recovers more than 90% of phosphorus, while sulphuric acid (H2SO4) recovers around 30%. Therefore, a chemical assessment of the remaining waste is a necessary step prior to initiating any remediation process. ERT provided the geometry of the deposits and the distribution of the phosphogypsum. The chemical analyses consistently validated the electrical contrast found within the deposits. We employed a correlation analysis combined with multivariate analysis to identify the relationships among the metal concentrations and resistivity. Principal component analysis (PCA) reduced the information contained in all the variables to a few principal components. The first three principal components accounted for 74% of the variability of all the studied variables. Partial least squares-discriminant analysis (PLS-DA) for classification allowed the discrimination of the two populations. Electrical resistivity was the most influential variable for separating HCl waste from that of H2SO4. The use of ERT saves time and reduces costs yielding a methodology which facilitates the environmental assessment of large areas.

Enhancing Electrical Contact with a Commercial Polymer for Electrical Resistivity Tomography on Archaeological Sites: A Case Study.

This communication reports an improvement of the quality of the electrical data obtained from the application of electrical resistivity tomography method on archaeological studies. The electrical contact between ground and electrode enhances significantly by using carbomer-based gel during the electrical resistivity tomography measurements. Not only does the gel promote the conservation of the building surface under investigation, but it also virtually eliminates the necessity of conventional spike electrodes, which in many archaeological studies are inadequate or not permitted. Results evidenced an enhancement in the quality of the electrical data obtained in the order of thousands of units compared with those without using the carbomer-based gel. The potential and capabilities of this affordable gel make it appropriate to be applied to other geoelectrical studies beyond archaeological investigations. Moreover, it might solve corrosion issues on conventional spike electrodes, and electrical multicore cables usually provoked for added saltwater attempting to improve the electrical contact.

Volumetric characterisation of waste deposits generated during the production of fertiliser derived from phosphoric rock by using LiDAR and electrical resistivity tomography.

The lack of environmental regulations before 1998 brought about significant ecological consequences in Europe. There are 4,000,000 ha potentially contaminated by waste, resulting from industrial activity. These sites present abnormal values of salinity, alkalinity, and organic and inorganic pollutants. A representative example of this is the production of fertiliser derived from phosphoric rock through the wet acid process which mainly produces phosphogypsum and pyrite ashes. For each tonne of fertiliser produced, five tonnes of phosphogypsum are generated, which in most of the cases were simply piled into non-conditionate deposits; currently, there is no information about these deposits. Hence, fast and affordable methodologies should be developed to calculate the volume contained in the existing waste deposits. Thus, this study aims to: i) scan the industrial area selected in order to identify the distribution and possible variants of the waste that make up the configuration of geoelectrical profiles and boreholes, and ii) accurately determine the volume contained in the chosen deposits by merging Electrical Resistivity Tomography with LiDAR point cloud. Results show a range of deposit volumes that run from 4900 m3 to 59,300 m3. The density of LiDAR point cloud (1 point/m2) ensures metric precision for the superficial layer. The Electrical Resistivity Tomography revealed the geometry of the deposits that superposes two layers marking the border between waste and natural terrain, and the boreholes guarantee the exactness of the waste layer thickness. This synergistic combination of Electrical Resistivity Tomography with LiDAR point cloud yields an accurate method that we used to calculate the volume of waste present in the deposits.

Predicting spatial distribution of heavy metals in an abandoned phosphogypsum pond combining geochemistry, electrical resistivity tomography and statistical methods.

One of the wastes generated in fertiliser production from phosphoric rock is phosphogypsum, whose mismanagement lead to environmental and health risks. Therefore, a detailed evaluation of the chemical composition of phosphogypsum is necessary to determine effective means of its management. Due to the high amount of generated waste, the cost and time consumed for this characterisation by chemical analysis is limiting. Hence, efficient tools should be developed to predict the chemical composition of this waste. Thus, this study aims to: 1) determine the physic-chemical characterisation of phosphogypsum pond using geochemical and geophysical techniques and 2) predict the heavy metals spatial distribution through statistical models. Results show that the most concentrate metal is chromium with a maximum of ≈900 mg.kg-1 and cadmium is the least concentrated (maximum ≈23 mg.kg-1). The Electrical Resistivity Tomography revealed the superposition of two layers. The top one (waste) presents low resistivity (≈17Ω.m) while the bottom layer shows higher resistivity (>124Ω.m). Metal concentrations and resistivities were combined by applying non-linear regression models. Cr showed the strongest correlation (R2 = 0.68), yielding an accurate model that was used for revealing the spatial distribution of the highest Cr concentrations in the pond, with the consequent reduction of expensive traditional methods.