A geoelectrical pre-study of Älvkarleby test embankment dam: 3D forward modelling and effects of structural constraints on the 3D inversion model of zoned embankment dams

Norooz, Reyhaneh; Olsson, Per-Ivar; Dahlin, Torleif; Günther, Thomas; Bernstone, Christian

doi:10.1016/j.jappgeo.2021.104355

Cited by 13 publications

(7 citation statements)

References 20 publications

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“…At present, it has achieved good application results in dynamic detection of oil and gas reservoirs, groundwater pollution, leak detection of dams, and leak detection of hazardous substances in landfills (Johnson et al, 2021;Norooz et al, 2021;Zhang et al, 2022). In the process of open pit mining, the rock and soil mass of the slope is always in a state of deformation, and its internal electrical structure also changes in real time.…”

Section: Introductionmentioning

confidence: 99%

Detection and evolution of disaster sources in high slopes of open-pit mines based on time-lapse high-density electricity method

Sen,

Zihao

2023

Front. Earth Sci.

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High and steep slopes in open pit mines used for ore production require careful monitoring to ensure stability and safeguard lives, property, and social welfare. Understanding the evolutionary characteristics of hazard sources within these slopes is crucial for effective slope management. This article presents a novel approach to identify the evolution of hazard sources within slopes by utilizing the derivative of normalized inversion data versus time as a characteristic feature. To analyze the evolution of hazard sources, raw apparent resistivity data collected at different times are processed using smoothing and wavelet denoising algorithms to reduce noise. Geological constraints are then applied to the collected data through the parametric region method. The least squares method is employed for independent inversion, and the inversion results are standardized. Subsequently, the standardized inversion results are used to derive the derivative of resistivity versus time equation, which reveals the evolution of hazard sources within the slope. A case study is conducted on a large open pit rocky slope to analyze the effectiveness of the proposed approach. The results demonstrate the successful reduction of noise through the smoothing and wavelet threshold denoising algorithms, effectively suppressing interference caused by acquisition equipment and environmental electromagnetic noise. The application of the parametric region method with geological constraints highlights the spatial and temporal evolution characteristics of hazard sources within the slope. The findings indicate varying degrees of evolution of hazard sources within the slope, with significant changes observed mainly in the shallow part influenced by water erosion and blasting vibration. Areas with lithological stability show minimal changes, while original hazard source areas have evolved to different extents, distinguishable based on the size and sparsity of contour values. By combining inversion data analysis, noise reduction techniques, and geological constraints, the proposed approach provides valuable insights into the evolution of hazard sources within slopes. This information can aid in the development of effective slope management strategies in open pit mines, contributing to enhanced safety and protection of valuable assets.

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Section: Introductionmentioning

confidence: 99%

Detection and evolution of disaster sources in high slopes of open-pit mines based on time-lapse high-density electricity method

Sen,

Zihao

2023

Front. Earth Sci.

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“…The electrical resistivity tomography (ERT) method is one of the most widely used geophysical techniques that provides the subsurface resistivity distribution for a variety of hydrogeological, environmental and engineering problems (e.g. Rucker et al ., 2011; Uhlemann et al ., 2015; Moradipour et al ., 2016; Whiteley et al ., 2017; Bièvre et al ., 2018; Hojat et al ., 2019a; Dahlin, 2020; Hermans and Paepen, 2020; Aleardi et al ., 2020; Loke et al ., 2020; Norooz et al ., 2021). Due to incomplete data coverage and noise contamination, the ERT is an ill‐posed problem affected by non‐uniqueness and instability of the solution (i.e., small variations of the data produce large perturbations in the predictions), and hence, an accurate estimation of the model uncertainty is of primary importance.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic inversions of electrical resistivity tomography data with a machine learning‐based forward operator

Aleardi

Vinciguerra

Stucchi

et al. 2022

Geophysical Prospecting

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Casting a geophysical inverse problem into a Bayesian setting is often discouraged by the computational workload needed to run many forward modelling evaluations. Here we present probabilistic inversions of electrical resistivity tomography data in which the forward operator is replaced by a trained residual neural network that learns the non‐linear mapping between the resistivity model and the apparent resistivity values. The use of this specific architecture can provide some advantages over standard convolutional networks as it mitigates the vanishing gradient problem that might affect deep networks. The modelling error introduced by the network approximation is properly taken into account and propagated onto the estimated model uncertainties. One crucial aspect of any machine learning application is the definition of an appropriate training set. We draw the models forming the training and validation sets from previously defined prior distributions, while a finite element code provides the associated datasets. We apply the approach to two probabilistic inversion frameworks: A Markov chain Monte Carlo algorithm is applied to synthetic data, while an ensemble‐based algorithm is employed for the field measurements. For both the synthetic and field tests, the outcomes of the proposed method are benchmarked against the predictions obtained when the finite element code constitutes the forward operator. Our experiments illustrate that the network can effectively approximate the forward mapping even when a relatively small training set is created. The proposed strategy provides a forward operator that is three orders of magnitude faster than the accurate but computationally expensive finite element code. Our approach also yields the most likely solutions and uncertainty quantifications comparable to those estimated when the finite element modelling is employed. The presented method allows solving the Bayesian electrical resistivity tomography with a reasonable computational cost and limited hardware resources.

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“…The electrical resistivity tomography (ERT) is widely used in a variety of hydrogeological, environmental and engineering problems to infer the subsurface resistivity values (Rucker et al ., 2011; Uhlemann et al ., 2015; Moradipour et al ., 2016; Whiteley et al ., 2017; Bièvre et al ., 2018; Hojat et al ., 2019a; Dahlin, 2020; Hermans and Paepen, 2020; Aleardi et al ., 2020; Loke et al ., 2020; Norooz et al ., 2021). Due to incomplete data coverage and noise contamination, the ERT is an ill‐posed problem affected by non‐uniqueness and instability (i.e., small variations of the data produce large perturbations in the predictions), and hence, an accurate estimation of the model uncertainty is of primary importance.…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐accelerated gradient‐based Markov chain Monte Carlo inversion applied to electrical resistivity tomography

Aleardi

Vinciguerra

Stucchi

et al. 2022

Near Surface Geophysics

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Expensive forward model evaluations and the curse of dimensionality usually hinder applications of Markov chain Monte Carlo algorithms to geophysical inverse problems. Another challenge of these methods is related to the definition of an appropriate proposal distribution that simultaneously should be inexpensive to manipulate and a good approximation of the posterior density. Here we present a gradient-based Markov chain Monte Carlo inversion algorithm that is applied to cast the electrical resistivity tomography into a probabilistic framework. The sampling is accelerated by exploiting the Hessian and gradient information of the negative log-posterior to define a proposal that is a local, Gaussian approximation of the target posterior probability. On the one hand, the computing time to run the many forward evaluations needed for both the data likelihood evaluation and the Hessian and gradient computation is decreased by training a residual neural network to predict the forward mapping between the resistivity model and the apparent resistivity value. On the other hand, the curse of dimensionality issue and the computational effort related to the Hessian and gradient manipulation are decreased by compressing data and model spaces through a discrete cosine transform. A non-parametric distribution is assumed as the prior probability density function. The method is first demonstrated on synthetic data and then applied to field measurements. The outcomes provided by the presented approach are also benchmarked against those obtained when a computationally expensive finiteelement code is employed for forward modelling , with the results of a gradient-free Markov chain Monte Carlo inversion, and also compared with the predictions of a deterministic inversion. The implemented approach not only guarantees uncertainty assessments and model predictions comparable with those achieved by more standard inversion strategies, but also drastically decreases the computational cost of the probabilistic inversion, making it similar to that of a deterministic inversion.

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A geoelectrical pre-study of Älvkarleby test embankment dam: 3D forward modelling and effects of structural constraints on the 3D inversion model of zoned embankment dams

Cited by 13 publications

References 20 publications

Detection and evolution of disaster sources in high slopes of open-pit mines based on time-lapse high-density electricity method

Detection and evolution of disaster sources in high slopes of open-pit mines based on time-lapse high-density electricity method

Probabilistic inversions of electrical resistivity tomography data with a machine learning‐based forward operator

Machine learning‐accelerated gradient‐based Markov chain Monte Carlo inversion applied to electrical resistivity tomography

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