Laterally constrained inversion (LCI) of multi-configuration EMI data with tunable sharpness

Klose, Tim; Guillemoteau, Julien; Vignoli, Giulio; Tronicke, Jens

doi:10.1016/j.jappgeo.2021.104519

Cited by 19 publications

(18 citation statements)

References 55 publications

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“…Here, it is important to note that S depends on m s¡1 . In order to facilitate the α search at each iteration by keeping it in the same range, we follow Guillemoteau et al (2022) and Klose et al (2022) by normalizing S as follows:…”

Section: Theory 70mentioning

confidence: 99%

“…The 2D synthetic data set has been simulated with a 3D non-linear forward modeling method based on the finite volume approach (Haber 2014). We use the input subsurface model studied in Klose et al (2022), which consists of two layers with an interface depth varying between 0.3 m and 1.5 m along 220 the profile (Fig. 3a).…”

Section: D Synthetic Data Examplementioning

confidence: 99%

“…Frequency-domain electromagnetic induction (FD-EMI) data collected by loop-loop sensors are widely used to investigate near-surface electrical properties, in particular electrical conductivity σ. Typical fields of application include archeological prospection (De Smedt et al 2014;Guillemoteau et al 2019;Kristiansen et al 2022), precision agriculture (Jadoon et al 2015;Rudolph et al 2016;Brogi 5 et al 2019;von Hebel et al 2021), hydrological studies (Vereecken et al 2015;von Hebel et al 2014;Martini et al 2017;Paepen et al 2020), mapping subsurface utilities (Guillemoteau & Tronicke 2015;Thiesson et al 2018;Couchman & Everett 2022), and exploring peat deposits (Altdorff et al 2016;Guillemoteau et al 2017;Beucher et al 2020;Clément et al 2020;McLachlan et al 2020;Klose et al 2022). Inversion of FD-EMI data is a typical example of a ill-posed problem, entailing the need…”

Section: Introductionmentioning

confidence: 99%

“…stabilizer (Portniaguine & Zhdanov 1999;Zhdanov 2002), which allows retrieving sparse/sharp solu-20 tions. Such MGS regularization strategies have been proposed for the inversion of gravity data (Last & Kubik 1983), electrical resistivity tomography (ERT) data (Blaschek et al 2008;Fiandaca et al 2015;Thibaut et al 2021), seismic dispersion curves (Vignoli et al 2021;Guillemoteau et al 2022), traveltime tomography (Zhdanov et al 2006;Ajo-Franklin et al 2007;Vignoli et al 2012), and FD-EMI data (Deidda et al 2020;Klose et al 2022). In the MGS schemes, the level of sparsity can be easily 25 tuned by means of the so-called focusing parameter.…”

Section: Introductionmentioning

confidence: 99%

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Structurally constrained inversion by means of a Minimum Gradient Support regularizer: examples of FD-EMI data inversion constrained by GPR reflection data

Klose

Guillemoteau

Vignoli

et al. 2023

Geophysical Journal International

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Summary Many geophysical inverse problems are known to be ill-posed and, thus, requiring some kind of regularization in order to provide a unique and stable solution. A possible approach to overcome the inversion ill-posedness consists in constraining the position of the model interfaces. For a grid-based parameterization, such a structurally-constrained inversion can be implemented by adopting the usual smooth regularization scheme in which the local weight of the regularization is reduced where an interface is expected. By doing so, sharp contrasts are promoted at interface locations while standard smoothness constraints keep affecting the other regions of the model. In this work, we present a structurally-constrained approach and test it on the inversion of frequency-domain electromagnetic induction (FD-EMI) data using a regularization approach based on the Minimum Gradient Support (MGS) stabilizer, which is capable to promote sharp transitions everywhere in the model, i.e., also in areas where no structural a priori information is available. Using 1D and 2D synthetic data examples, we compare the proposed approach to a structurally-constrained smooth inversion as well as to more standard (i.e., not structurally-constrained) smooth and sharp inversions. Our results demonstrate that the proposed approach helps in finding a better and more reliable reconstruction of the subsurface electrical conductivity distribution, including its structural characteristics. Furthermore, we demonstrate that it allows to promote sharp parameter variations in areas where no structural information are available. Lastly, we apply our structurally-constrained scheme to FD-EMI field data collected at a field site in Eastern Germany to image the thickness of peat deposits along two selected profiles. In this field example, we use collocated constant offset ground-penetrating radar (GPR) data to derive structural a priori information to constrain the inversion of the FD-EMI data. The results of this case study demonstrate the effectiveness and flexibility of the proposed approach.

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Section: Theory 70mentioning

confidence: 99%

Section: D Synthetic Data Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Structurally constrained inversion by means of a Minimum Gradient Support regularizer: examples of FD-EMI data inversion constrained by GPR reflection data

Klose

Guillemoteau

Vignoli

et al. 2023

Geophysical Journal International

Self Cite

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“…The regularization, φ r ( ), is used to incorporate prior knowledge into the imaging process, and it varies in different tasks. For instance, in geophysical or biomedical imaging, to emphasize the sharpness or smoothness of material boundaries, 1 and 2 norms of the spatial gradient of are usually adopted [29]- [33]. In radar imaging, the sparsity of the observed scene is often exploited to improve imaging quality by incorporating sparsity regularization, such as the 1 norm given by φ r ( ) = 1 [34]- [36].…”

Section: Formulations and Challenges Of Em Imagingmentioning

confidence: 99%

Physics Embedded Machine Learning for Electromagnetic Data Imaging

Guo¹,

Huang²,

Li³

et al. 2022

Preprint

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Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usually computationally expensive.Machine learning (ML) techniques and especially deep learning (DL) show potential in fast and accurate imaging. However, the high performance of purely data-driven approaches relies on constructing a training set that is statistically consistent with practical scenarios, which is often not possible in EM imaging tasks. Consequently, generalizability becomes a major concern. On the other hand, physical principles underlie EM phenomena and provide baselines for current imaging techniques. To benefit from prior knowledge in big data and the theoretical constraint of physical laws, physics embedded ML methods for EM imaging have become the focus of a large body of recent work. This article surveys various schemes to incorporate physics in learning-based EM imaging. We first introduce background on EM imaging and basic formulations of the inverse problem. We then focus on three types of strategies combining physics and ML for linear and nonlinear imaging and discuss their advantages and limitations. Finally, we conclude with open challenges and possible ways forward in this fast-developing field. Our aim is to facilitate the study of intelligent EM imaging methods that will be efficient, interpretable and controllable.

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Tracing past extreme floods on an alluvial fan using geophysical surveying

Arboleda‐Zapata,

Guillemoteau,

Lucía

et al. 2023

Earth Surf Processes Landf

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SummarySedimentary units in alluvial fans may record gradual transport and deposition during multiple floods or sediment‐laden flows or, conversely, during few catastrophic events. While outcrops are a valuable source of direct information to constrain past geomorphic and hydrologic processes, such exposures are scarce, especially along aggrading rivers or those that have been subject to recent catastrophic sedimentation. In this context, near‐surface geophysical techniques can constrain the dimensions, internal architecture, composition, and petrophysical properties of different sedimentary units. We consider the Grimmbach alluvial fan in the cuesta landscape of southwestern Germany, which was heavily impacted by sediment and wood loads during a flash flood in 2016; published radiocarbon dates indicate that at least three floods similar to the one in 2016 may have occurred since the 17th century. To test whether and to which detail near‐surface geophysics might reveal the sedimentary legacy of these floods, we survey the Grimmbach alluvial fan using detailed topographic data and geophysical imaging based on electromagnetic induction, electrical resistivity tomography (ERT), and ground‐penetrating radar. Our geophysical results indicate former channel courses and two coarse bar deposits up to 3 m below the surface, which are comparable with the more extensive bar deposits of the 2016 flood. From the ERT models, we interpret coarse, up to 5 m thick, gravel lag overlying bedrock at a maximum depth of 10 m. Our geophysical results also highlight patches of finer materials derived from gradual sedimentation and soil development. Overall, our results show that the Grimmbach alluvial fan may have formed and reshaped during catastrophic flows, which likely caused channel avulsions. Our findings point to the need to reconsider flash flood and debris‐flow hazards in similar headwaters and fans of this seemingly quiescent cuesta landscape in southern Germany.

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Laterally constrained inversion (LCI) of multi-configuration EMI data with tunable sharpness

Cited by 19 publications

References 55 publications

Structurally constrained inversion by means of a Minimum Gradient Support regularizer: examples of FD-EMI data inversion constrained by GPR reflection data

Structurally constrained inversion by means of a Minimum Gradient Support regularizer: examples of FD-EMI data inversion constrained by GPR reflection data

Physics Embedded Machine Learning for Electromagnetic Data Imaging

Tracing past extreme floods on an alluvial fan using geophysical surveying

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