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.
Highlights
Airborne time-domain electromagnetics (ATEM) was originally developed for mineral prospection [1,2,3,4] and is a very consolidated methodology
We perform two synthetic tests of increasing complexity to investigate the effects of including in the inversion process: (i) the proper prior information, and (ii) an estimation of the modeling error
We compare our results against a solution provided by a more standard 1D deterministic inversion
Summary
Airborne time-domain electromagnetics (ATEM) was originally developed for mineral prospection [1,2,3,4] and is a very consolidated methodology. Technological advancements made it possible to move from the simple mineral target detection to more detailed groundwater mapping [21,22] and geological modeling applications [23,24]. To what happens in seismics (e.g., [25]), the stacking of the recorded transient curves—by means of moving windows whose widths depend on the time-gate [26,27,28]—can be used to preserve high lateral resolution at shallow, while increasing the reliability of the data at depth; this is, compatible, at the same time: (i) with the higher signal-to-noise at the early-gates, and (ii) with the larger spatial footprint at the late-gates
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