@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}
}
@article{ArboledaZapataAngelopoulosOverduinetal.2022,
  author    = {Arboleda-Zapata, Mauricio and Angelopoulos, Michael and Overduin, Pier Paul and Grosse, Guido and Jones, Benjamin M. and Tronicke, Jens},
  title     = {Exploring the capabilities of electrical resistivity tomography to study subsea permafrost},
  series = {The Cryosphere},
  volume    = {16},
  journal   = {The Cryosphere},
  publisher = {Copernicus},
  address   = {Katlenburg-Lindau},
  issn      = {1994-0424},
  doi       = {10.5194/tc-16-4423-2022},
  pages     = {4423 -- 4445},
  year      = {2022},
  abstract  = {Sea level rise and coastal erosion have inundated large areas of Arctic permafrost. Submergence by warm and saline waters increases the rate of inundated permafrost thaw compared to sub-aerial thawing on land. Studying the contact between the unfrozen and frozen sediments below the seabed, also known as the ice-bearing permafrost table (IBPT), provides valuable information to understand the evolution of sub-aquatic permafrost, which is key to improving and understanding coastal erosion prediction models and potential greenhouse gas emissions. In this study, we use data from 2D electrical resistivity tomography (ERT) collected in the nearshore coastal zone of two Arctic regions that differ in their environmental conditions (e.g., seawater depth and resistivity) to image and study the subsea permafrost. The inversion of 2D ERT data sets is commonly performed using deterministic approaches that favor smoothed solutions, which are typically interpreted using a user-specified resistivity threshold to identify the IBPT position. In contrast, to target the IBPT position directly during inversion, we use a layer-based model parameterization and a global optimization approach to invert our ERT data. This approach results in ensembles of layered 2D model solutions, which we use to identify the IBPT and estimate the resistivity of the unfrozen and frozen sediments, including estimates of uncertainties. Additionally, we globally invert 1D synthetic resistivity data and perform sensitivity analyses to study, in a simpler way, the correlations and influences of our model parameters. The set of methods provided in this study may help to further exploit ERT data collected in such permafrost environments as well as for the design of future field experiments.},
  language  = {en}
}
@misc{ArboledaZapataAngelopoulosOverduinetal.2022,
  author    = {Arboleda-Zapata, Mauricio and Angelopoulos, Michael and Overduin, Pier Paul and Grosse, Guido and Jones, Benjamin M. and Tronicke, Jens},
  title     = {Exploring the capabilities of electrical resistivity tomography to study subsea permafrost},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1285},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-57123},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-571234},
  pages     = {4423 -- 4445},
  year      = {2022},
  abstract  = {Sea level rise and coastal erosion have inundated large areas of Arctic permafrost. Submergence by warm and saline waters increases the rate of inundated permafrost thaw compared to sub-aerial thawing on land. Studying the contact between the unfrozen and frozen sediments below the seabed, also known as the ice-bearing permafrost table (IBPT), provides valuable information to understand the evolution of sub-aquatic permafrost, which is key to improving and understanding coastal erosion prediction models and potential greenhouse gas emissions. In this study, we use data from 2D electrical resistivity tomography (ERT) collected in the nearshore coastal zone of two Arctic regions that differ in their environmental conditions (e.g., seawater depth and resistivity) to image and study the subsea permafrost. The inversion of 2D ERT data sets is commonly performed using deterministic approaches that favor smoothed solutions, which are typically interpreted using a user-specified resistivity threshold to identify the IBPT position. In contrast, to target the IBPT position directly during inversion, we use a layer-based model parameterization and a global optimization approach to invert our ERT data. This approach results in ensembles of layered 2D model solutions, which we use to identify the IBPT and estimate the resistivity of the unfrozen and frozen sediments, including estimates of uncertainties. Additionally, we globally invert 1D synthetic resistivity data and perform sensitivity analyses to study, in a simpler way, the correlations and influences of our model parameters. The set of methods provided in this study may help to further exploit ERT data collected in such permafrost environments as well as for the design of future field experiments.},
  language  = {en}
}