Introducing the concept of supervised stacking to improve the signal-to-noise ratio: application to airborne electromagnetic data

Geophysics provides useful information of the subsurface for many applications. However, noise generally affects the dataset, which may prevent the acquisition of usable data. Stacking techniques can be used in an attempt to improve the signal-to-noise ratio. However, stacking is generally applied without any control on the raw data taken into account, and can be ineffective given the nature of noise that can affect the dataset. To a lesser extent, arbitrarily increasing the stack size may also be ineffective, especially in anthropized environments. This paper introduces a supervised stacking method that stacks raw data considering different combinations, and then estimates the signal-to-noise ratio of the resulting stacked data. The estimation of the signal-to-noise ratio uses the singular value decomposition filtering, which has proven to be effective in identifying noise affecting AEM data. The stacking method is applied to the raw data of three different AEM datasets acquired in noisy resistive environments. The results show that the method improves the signal-to-noise ratio and can reduce high-amplitude noise. It provides less noisy data for post-processing and offers new processing possibilities.