Sensitivity-based Selection of Electrode Configurations for Landfill ERT Monitoring: Modelling Tests

Summary Electrical resistivity tomography (ERT) is a valuable non-invasive technique for monitoring municipal solid waste landfills. The geometry of the used electrode configurations can be tuned to enhance sensitivity and resolution, enabling optimization for specific subsurface features and reducing field operations and processing time. However, landfill-specific challenges, such as large site sizes and electrode placement above or around waste, limit depth of investigation and spatial resolution in critical zones, like beneath the waste mass. Sensitivity declines with increased distance from electrodes, complicating data acquisition. Additionally, incorporating high-resistivity liners usually placed below landfills into geoelectrical numerical models can be computationally expensive and exacerbate the ill-posed nature of the inverse problem. To overcome these challenges, we use a mixed-dimensional numerical approach for efficient sensitivity analysis and the selection of optimal electrode configurations for investigating regions beneath the liner.

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Published
2025-01-01
DOI
https://doi.org/10.3997/2214-4609.202572169
Primary Topic
Industrial Vision Systems and Defect Detection
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article
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article

Sensitivity-based Selection of Electrode Configurations for Landfill ERT Monitoring: Modelling Tests

L. Zanzi, Laura Longoni, Lorenzo Panzeri, Monica Papini et al.
Industrial Vision Systems and Defect Detection
article

Sensitivity-based Selection of Electrode Configurations for Landfill ERT Monitoring: Modelling Tests

L. Zanzi, Laura Longoni, Lorenzo Panzeri, Monica Papini, Diego Arosio, Anna Fumagalli, Zhiwei Zhang
article en

Abstract

Summary Electrical resistivity tomography (ERT) is a valuable non-invasive technique for monitoring municipal solid waste landfills. The geometry of the used electrode configurations can be tuned to enhance sensitivity and resolution, enabling optimization for specific subsurface features and reducing field operations and processing time. However, landfill-specific challenges, such as large site sizes and electrode placement above or around waste, limit depth of investigation and spatial resolution in critical zones, like beneath the waste mass. Sensitivity declines with increased distance from electrodes, complicating data acquisition. Additionally, incorporating high-resistivity liners usually placed below landfills into geoelectrical numerical models can be computationally expensive and exacerbate the ill-posed nature of the inverse problem. To overcome these challenges, we use a mixed-dimensional numerical approach for efficient sensitivity analysis and the selection of optimal electrode configurations for investigating regions beneath the liner.

University of Modena and Reggio Emilia (IT), Politecnico di Milano (IT)
Openalex Percentile: Top 81%
Industrial Vision Systems and Defect Detection
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