Novel Airborne EM Image Appraisal Tool for Imperfect Forward Modelling

TL;DR

This paper introduces a computationally efficient appraisal tool for airborne electromagnetic data that identifies model zones incompatible with multidimensional data, improving interpretation reliability in quasi-2D/3D inversions.

Contribution

The paper presents a novel, inexpensive image appraisal method based on normalized gradients to detect zones where forward modelling assumptions fail.

Findings

Identifies problematic zones in AEM inversion models.

Demonstrates effectiveness in a salinization survey.

Enhances interpretation accuracy of quasi-2D/3D models.

Abstract

Full 3D inversion of time-domain Airborne ElectroMagnetic (AEM) data requires specialists' expertise and a tremendous amount of computational resources, not readily available to everyone. Consequently, quasi-2D/3D inversion methods are prevailing, using a much faster but approximate (1D) forward model. We propose an appraisal tool that indicates zones in the inversion model that are not in agreement with the multidimensional data and therefore, should not be interpreted quantitatively. The image appraisal relies on multidimensional forward modelling to compute a so-called normalized gradient. Large values in that gradient indicate model parameters that do not fit the true multidimensionality of the observed data well and should not be interpreted quantitatively. An alternative approach is proposed to account for imperfect forward modelling, such that the appraisal tool is computationally inexpensive. The method is demonstrated on an AEM survey in a salinization context, revealing possible problematic zones in the estimated fresh-saltwater interface.