@article{KloseGuillemoteauVignolietal.2022,
  author    = {Klose, Tim and Guillemoteau, Julien and Vignoli, Giulio and Tronicke, Jens},
  title     = {Laterally constrained inversion (LCI) of multi-configuration EMI data with tunable sharpness},
  series = {Journal of applied geophysics},
  volume    = {196},
  journal   = {Journal of applied geophysics},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0926-9851},
  doi       = {10.1016/j.jappgeo.2021.104519},
  pages     = {9},
  year      = {2022},
  abstract  = {Frequency-domain electromagnetic (FDEM) data are commonly inverted to characterize subsurface geoelectrical properties using smoothness constraints in 1D inversion schemes assuming a layered medium. Smoothness constraints are suitable for imaging gradual transitions of subsurface geoelectrical properties caused, for example, by varying sand, clay, or fluid content. However, such inversion approaches are limited in characterizing sharp interfaces. Alternative regularizations based on the minimum gradient support (MGS) stabilizers can, instead, be used to promote results with different levels of smoothness/sharpness selected by simply acting on the so-called focusing parameter. The MGS regularization has been implemented for different kinds of geophysical data inversion strategies. However, concerning FDEM data, the MGS regularization has only been implemented for vertically constrained inversion (VCI) approaches but not for laterally constrained inversion (LCI) approaches. We present a novel LCI approach for FDEM data using the MGS regularization for the vertical and lateral direction. Using synthetic and field data examples, we demonstrate that our approach can efficiently and automatically provide a set of model solutions characterized by different levels of sharpness and variable lateral consistencies. In terms of data misfit, the obtained set of solutions contains equivalent models allowing us also to investigate the non-uniqueness of FDEM data inversion.},
  language  = {en}
}
@article{GuillemoteauVignoliBarretoetal.2022,
  author    = {Guillemoteau, Julien and Vignoli, Giulio and Barreto, Jeniffer and Sauvin, Guillaume},
  title     = {Sparse laterally constrained inversion of surface-wave dispersion curves via minimum gradient support regularization},
  series = {Geophysics},
  volume    = {87},
  journal   = {Geophysics},
  number    = {3},
  publisher = {Society of Exploration Geophysicists},
  address   = {Tulsa, Okla.},
  issn      = {0016-8033},
  doi       = {10.1190/GEO2021-0247.1},
  pages     = {R281 -- R289},
  year      = {2022},
  abstract  = {We have developed a 1D laterally constrained inversion of surface-wave dispersion curves based on the minimum gradient support regularization, which allows solutions with tunable sharpness in the vertical and horizontal directions. The forward modeling consists of a finite-elements approach incorporated in a flexible nonparametric gradient-based inversion scheme, which has already demonstrated good stability and convergence capabilities when tested on other kinds of data. Our deterministic inversion procedure is performed in the shear-wave velocity log space as we noticed that the associated Jacobian indicates a reduced model dependency, and this, in turn, decreases the risks of local nonconvexity. We show several synthetics and one field example to demonstrate the effectiveness and the applicability of the proposed approach.},
  language  = {en}
}
@article{ArboledaZapataGuillemoteauTronicke2022,
  author    = {Arboleda-Zapata, Mauricio and Guillemoteau, Julien and Tronicke, Jens},
  title     = {A comprehensive workflow to analyze ensembles of globally inverted 2D electrical resistivity models},
  series = {Journal of applied geophysics},
  volume    = {196},
  journal   = {Journal of applied geophysics},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0926-9851},
  doi       = {10.1016/j.jappgeo.2021.104512},
  pages     = {12},
  year      = {2022},
  abstract  = {Electrical resistivity tomography (ERT) aims at imaging the subsurface resistivity distribution and provides valuable information for different geological, engineering, and hydrological applications. To obtain a subsurface resistivity model from measured apparent resistivities, stochastic or deterministic inversion procedures may be employed. Typically, the inversion of ERT data results in non-unique solutions; i.e., an ensemble of different models explains the measured data equally well. In this study, we perform inference analysis of model ensembles generated using a well-established global inversion approach to assess uncertainties related to the nonuniqueness of the inverse problem. Our interpretation strategy starts by establishing model selection criteria based on different statistical descriptors calculated from the data residuals. Then, we perform cluster analysis considering the inverted resistivity models and the corresponding data residuals. Finally, we evaluate model uncertainties and residual distributions for each cluster. To illustrate the potential of our approach, we use a particle swarm optimization (PSO) algorithm to obtain an ensemble of 2D layer-based resistivity models from a synthetic data example and a field data set collected in Loon-Plage, France. Our strategy performs well for both synthetic and field data and allows us to extract different plausible model scenarios with their associated uncertainties and data residual distributions. Although we demonstrate our workflow using 2D ERT data and a PSObased inversion approach, the proposed strategy is general and can be adapted to analyze model ensembles generated from other kinds of geophysical data and using different global inversion approaches.},
  language  = {en}
}