A new workflow to incorporate prior information in minimum gradient support (MGS) inversion of electrical resistivity and induced polarization data

Thibaut, Robin; Kremer, Thomas; Royen, Annie; Ngun, Bun Kim; Nguyen, Frédéric; Hermans, Thomas

doi:10.1016/j.jappgeo.2021.104286

Cited by 16 publications

(6 citation statements)

References 44 publications

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“…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%

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: Introductionmentioning

confidence: 99%

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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“…", e.g., Van Hoorde et al (2017) • "Where to drill to cross a deposit's mineralizations? ", e.g., Evrard et al (2018); Thibaut et al (2021a).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Bayesian Experimental Design in the Subsurface

Thibaut¹

2023

Preprint

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“…Ikard and Pease [ 8 ] identified the preferential channels of groundwater seepage in porous media from self-potential data. Thibaut et al [ 9 ] proposed a new workflow to apply the Minimum Gradient Support (MGS) to real case studies where heterogeneous structures are expected. More recently, Hermans et al [ 10 ] put forward 4D imaging techniques thus to advocate a more systematic characterization of the dynamic and 3D nature of the subsurface for a series of critical processes and emerging applications.…”

Section: Introductionmentioning

confidence: 99%