Unscented Kalman filter assimilation of time-lapse self-potential data for monitoring solute transport

Cui, Yi-an; Liu, Lanbo; Zhu, Xiaoxiong

doi:10.1088/1742-2140/aa7306

Cited by 16 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More specifically, contamination studies have recently multiplied due to the high sensitivity of SP to redox conditions in shallow aquifers [10][11][12]. Indeed, spontaneous potential detectable at the soil surface, is sensitive to spatial variations in charge flow within the matrix, oxidation-reduction gradients, temperature variations, microorganism signals or other activities accompanied by contaminant migration [13][14][15][16][17][18][19]. The delimitation of a plume also makes it possible to locate groundwater access points more precisely for the characterisation or monitoring of the microbiological and chemical status of the groundwater [20,21].…”

Section: Introductionmentioning

confidence: 99%

Landfill Pollution Plume Survey in the Moroccan Tadla Using Spontaneous Potential

Mouine

Hamdi

Morarech

et al. 2021

Water

View full text Add to dashboard Cite

In many parts of the world, the impact of open landfills on soils, biosphere, and groundwater has become a major concern. These landfills frequently generate pollution plumes, the contours of which can be delineated by non-intrusive geophysical measurements, but in arid environments, the high soils resistivity is usually an obstacle, which results in the low number of studies that have been carried out there. In addition, such prospecting using geophysical techniques do not provide information on the intensity of the processes occurring in the water table. This study was carried out on an uncontrolled landfill in the arid Tadla plain, Morocco’s main agricultural region. A survey based on geo-referenced spontaneous potential measurements was combined with measurements of anoxic conditions (Eh-pH and O2 equilibrating partial pressure) in the groundwater and leachates, in order to highlight a pollution plume and its geometry. The range of spontaneous potential measurement is wide, reaching 155 mV. Ponds of leachate with high electrical conductivity (20 to 40 mS cm−1) form within the landfill, and present very reducing conditions down to sulphate reduction and methanisation. The plume is slowly but continuously supplied with these highly reducing and organic carbon-rich leachates from the landfill. Its direction is towards N-NW, stable throughout the season, and consistent with local knowledge of groundwater flow. The fast flow of the water table suggests pollution over long distances that should be monitored in the future. The results obtained are spatially contrasting and stable, and show that such techniques can be used on a resistive medium of arid environments.

show abstract

Section: Introductionmentioning

confidence: 99%

Landfill Pollution Plume Survey in the Moroccan Tadla Using Spontaneous Potential

Mouine

Hamdi

Morarech

et al. 2021

Water

View full text Add to dashboard Cite

show abstract

“…In order to verify the effectiveness of the PINN forward method, we first need to give a scenario, and use the FEM to calculate Eq. (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11), to obtain the distribution of hydraulic head and electric potential in the problem area as the reference solution. We set up a 2D rectangular area (Fig.…”

Section: Problem Description and Fem Solutionmentioning

confidence: 99%

“…In the numerical simulation of self-potential, a general method is to use the finite element method, by setting the boundary conditions and initial conditions of the whole region, solve the differential equation of the electric field, and obtain the electrical potential distribution of the whole region 6,7 . In recent years, data-driven discovery, including machine learning (ML), has been widely recognized by the academic community, and has become a popular tool in hydrology 8 .…”

Section: Introductionmentioning

confidence: 99%

Self-potential forward modeling method based on physics-informed neural networks

Yang

Nai

Gao

et al. 2022

International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022)

View full text Add to dashboard Cite

Self-potential method is a common method of environmental geophysical exploration. Researchers often use finite element method (FEM) to carry out forward modeling. Forward modeling is an important part of research, but the FEM has some problems, such as large amount of calculation, long time and so on. In this regard, we propose a method based on physics-informed neural networks (PINNs) for forward modeling of a kind of self-potential called streaming potential. The particularity of streaming potential is that its formation involves both flow field and electric field. In order to verify the feasibility of the PINNs method, we set up a two-dimensional (2D) scene and the corresponding boundary conditions, and tried to calculate the hydraulic head distribution and the corresponding self-potential distribution in the saturated and unsaturated flow region under stable conditions by using neural network. We have adopted two different network structure designs. One is to use the same neural network to calculate the hydraulic head distribution and electrical potential distribution in the region at the same time. The other is to use two different neural networks to calculate the hydraulic head and electrical potential respectively. In both methods, we have added the corresponding partial differential equation (PDE) to constrain the network training process. Then, we compare their results with the traditional FEM. We find that such a neural network with physical constraints can effectively obtain the spatial distribution of the unknown quantity we need. Compared with the FEM, PINNs method can be used in the case of incomplete boundary conditions. In addition, compared with other deep learning (DL) methods, the results of PINNs method are more interpretable.

show abstract

“…In the last decades, various interpretation techniques have been developed for the quantitative interpretation of SP data (e.g., [9,23,30,57,73] and references therein). Cui et al [18] used unscented Kalman filter (UKF) approach to assimilate the time-lapse self-potential data for subsurface contaminant monitoring.…”

Section: Self-potentialmentioning

confidence: 99%

Tracking contaminant transport using time-lapse geophysics: A review on applications of electrical methods

Rani

Cassiani

2022

Water Security

View full text Add to dashboard Cite

Unscented Kalman filter assimilation of time-lapse self-potential data for monitoring solute transport

Cited by 16 publications

References 26 publications

Landfill Pollution Plume Survey in the Moroccan Tadla Using Spontaneous Potential

Landfill Pollution Plume Survey in the Moroccan Tadla Using Spontaneous Potential

Self-potential forward modeling method based on physics-informed neural networks

Tracking contaminant transport using time-lapse geophysics: A review on applications of electrical methods

Contact Info

Product

Resources

About