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

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.

Probabilistic Petrophysical Reconstruction of Danta's Alpine Peatland via Electromagnetic Induction Data

“…Future developments should involve the assessment of the modeling error and the inclusion of such an inevitable source of uncertainty into the inversion scheme as has already been done for the time‐domain counterpart (Bai et al., 2021).…”

“…In the deterministic frameworks, a physical relationship mapping the rock properties to the geophysical parameters is often necessary (Cao et al., 2023; Foged et al., 2014; Mastrocicco et al., 2010). Instead, in the probabilistic context, like the one used in the present research, the connection between lithology and physical properties is formulated in statistical terms and inherently incorporates the associated uncertainties (here, the uncertainty is not the one characterizing the geophysical measurements, but, rather the one associated with the petrophysical link) (Bai, 2022; Grana, 2016, 2020; Grana & Della Rossa, 2010; Gulbrandsen et al., 2017; Madsen et al., 2023).…”

Use of Machine Learning to Estimate Statistics of the Posterior Distribution in Probabilistic Inverse Problems—An Application to Airborne EM Data

“…In the example application, we adopted a widely used uncorrelated Gaussian noise model. In practice, real data are often affected by correlated noise (Bai et al., 2021; Hansen et al., 2014; Hauser et al., 2015). While in principle any noise model can be handled by the proposed methodology, as long as realizations of the noise can be generated, it remains to be tested how well the methodology works with more complex noise models.…”

Instantaneous Inversion of Airborne Electromagnetic Data Based on Deep Learning

“…Naturally, data inversion has become the essential part of AEM survey, the result of which contributes significantly to our understanding of the subsurface structure. AEM inversion techniques can basically be categorized into either deterministic or stochastic methods (Bai et al., 2021; Blatter et al., 2018; Christiansen et al., 2016; Cox et al., 2012; Hansen, 2021; Hauser et al., 2015; Liu & Yin, 2016; McMillan et al., 2015; Minsley et al., 2021). Deterministic methods suffer from the inherent non‐uniqueness of the solution and are prone to instability, particularly under severe noise conditions.…”