1D Stochastic Inversion of Airborne Time-domain Electromag-netic Data with Realistic Prior and Accounting for the Forward Modeling Error

TL;DR

This paper introduces a stochastic 1D inversion method for airborne electromagnetic data that accounts for forward modeling errors, leading to more accurate and reliable subsurface conductivity models.

Contribution

It presents a novel probabilistic inversion approach that incorporates realistic prior information and explicitly models forward error, improving the fidelity and reliability of inversion results.

Findings

Enhanced reconstruction accuracy of subsurface targets.

Improved estimation of model uncertainty and reliability.

Better differentiation between true features and artifacts caused by modeling errors.

Abstract

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