1D Stochastic Inversion of Airborne Time-Domain Electromagnetic Data with Realistic Prior and Accounting for the Forward Modeling Error

Airborne electromagnetic surveys may consist of hundreds thousands soundings. In most cases, this makes 3D inversions unfeasible even when the subsurface is characterized by a high level heterogeneity. Instead, approaches based on 1D forwards are routinely used because their computational efficiency. However, it relatively easy to fit responses with forward modelling and retrieve apparently well-resolved conductivity models. those detailed features simply be caused fitting error connected approximate forward. addition, is, in practice, difficult identify kind artifacts as modeling correlated. The present study demonstrates how assess introduced approximation include additional piece information into probabilistic inversion. Not surprisingly, turns out that simple modification provides not only much better reconstructions targets but, maybe, more importantly, guarantees correct estimation corresponding reliability.