Abstract
The possibility to have results very quickly after, or even during, the collection of electromagnetic data would be important, not only for quality check purposes, but also for adjusting the location of the proposed flight lines during an airborne time-domain acquisition. This kind of readiness could have a large impact in terms of optimization of the Value of Information of the measurements to be acquired. In addition, the importance of having fast tools for retrieving resistivity models from airborne time-domain data is demonstrated by the fact that Conductivity-Depth Imaging methodologies are still the standard in mineral exploration. In fact, they are extremely computationally efficient, and, at the same time, they preserve a very high lateral resolution. For these reasons, they are often preferred to inversion strategies even if the latter approaches are generally more accurate in terms of proper reconstruction of the depth of the targets and of reliable retrieval of true resistivity values of the subsurface. In this research, we discuss a novel approach, based on neural network techniques, capable of retrieving resistivity models with a quality comparable with the inversion strategy, but in a fraction of the time. We demonstrate the advantages of the proposed novel approach on synthetic and field datasets.
Highlights
Airborne time-domain electromagnetic (ATEM) data have been collected for decades for mineral prospection [1,2,3,4]
In which (i) dobs is the vector of the observations; (ii) m is the vector of the model parameters; (iii) F is the forward modelling operator mapping the model m into the data space; F takes into account the physics of the process and the characteristics of the acquisition system [46]; (iv) Wd is the data weighting matrix taking into account the uncertainty in the measurements; (v) s(m) is the regularization term incorporating the prior knowledge about the resistivity model to be reconstructed; (vi) the multiplier λ controls the balance between the importance given to the data with respect to the prior information
We present a novel approach to the inversion of airborne time-domain electromagnetic data based on neural networks
Summary
Airborne time-domain electromagnetic (ATEM) data have been collected for decades for mineral prospection [1,2,3,4]. The capabilities of the available instrumentation allowed to move from the mere mineral target detection to more sophisticated geological modelling [20,21,22] and groundwater mapping [23,24] applications. The data processing and inversion strategies have gone through continuous advancements. In this respect, just to mention an example, stacking of the recorded transient curves via moving windows with widths that are time-gate dependent [25,26] can increase the lateral resolution at shallow, while enhancing the signal-to-noise ratio at depth (where, in any case, the physics of the methodology leads to larger observation footprints). Novel inversion strategies made it possible to enforce spatial coherence [27,28] and allowed the retrieval of (pseudo-)3D resistivity distribution even by using relatively simple 1D forward modelling [29,30]
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