Frequency-domain electromagnetic induction inversion with randomized tensor decomposition

Abstract

Near-surface systems can be complex and highly heterogeneous. The complex nature of these systems makes their numerical modelling a challenging problem in geosciences. Geophysical survey methods combined with direct measurements have been widely used to characterize the spatial distribution of the near-surface physical properties. Within this scope, geophysical inversion has been a preferable tool to predict quantitively the spatial distribution of the relevant near-surface properties. Ensemble-based data assimilation techniques are common geophysical inversion methods used in problems related to subsurface modelling and characterization. These methods allow the accurate prediction of the spatial distribution of the subsurface properties and have the ability to assess the uncertainty about the model predictions. However, these are computationally demanding inversion techniques, which makes their applicability to large data sets prohibitive.