Impact of an Uncertain Structural Constraint on Electrical Resistivity Tomography for Water Content Estimation in Landslides

Jasmin Grifka, Maximilian Weigand, Andreas Kemna and Thomas Heinze
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Jasmin Grifka: Department of Geophysics, Institute of Geosciences, University of Bonn, 53113 Bonn, Germany
Maximilian Weigand: Department of Geophysics, Institute of Geosciences, University of Bonn, 53113 Bonn, Germany
Andreas Kemna: Department of Geophysics, Institute of Geosciences, University of Bonn, 53113 Bonn, Germany
Thomas Heinze: Department of Geophysics, Institute of Geosciences, University of Bonn, 53113 Bonn, Germany

Land, 2022, vol. 11, issue 8, 1-13

Abstract: Geoelectrical methods can be part of early warning systems for landslide-prone hillslopes by giving estimates of the water content distribution. Structurally constrained inversions of geoelectrical data can improve the water content estimation by reducing the smoothness constraint along known lithological boundaries, which is especially important for landslides, as often layers with strongly divergent hydrological parameters and varying electrical signatures are present in landslides. However, any a priori information about those boundaries has an intrinsic uncertainty. A detailed synthetic study and a field investigation are combined to study the influence of misplaced structural constraints and the strength of the smoothness reduction via a coupling coefficient on inversion results of electrical resistivity data. While a well-known lithological boundary with a substantial reduction of the smoothness constraint can significantly improve the inversion result, a flawed constraint can cause strong divergences from the synthetic model. The divergence can even grow above the divergence of a fully smoothed inversion result. For correctly placed structural constraints, a coupling coefficient smaller than 10 − 4 uncovers previously unseen dynamics in the resistivity distribution compared to smoothed inversion results. Uncertain layer boundaries can be included in the inversion process with a larger coupling coefficient to avoid flawed results as long as the uncertainty of the layer thickness is below 20%. The application to field data confirms these findings but is less sensitive to a further reduction of the coupling coefficient, probably due to uncertainties in the structural information.

Keywords: electrical resistivity tomography; structural constraint inversion; water content; landslide; monitoring; early warning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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