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

Peatlands are fundamental deposits of organic carbon. Thus, their protection is of crucial importance to avoid emissions from their degradation. Peat is a mixture of organic soil that originates from the accumulation of wetland plants under continuous or cyclical anaerobic conditions for long periods. Hence, a precise quantification of peat deposits is extremely important; for that, remote- and proximal-sensing techniques are excellent candidates. Unfortunately, remote-sensing can provide information only on the few shallowest centimeters, whereas peatlands often extend to several meters in depth. In addition, peatlands are usually characterized by difficult (flooded) terrains. So, frequency-domain electromagnetic instruments, as they are compact and contactless, seem to be the ideal solution for the quantitative assessment of the extension and geometry of peatlands. Generally, electromagnetic methods are used to infer the electrical resistivity of the subsurface. In turn, the resistivity distribution can, in principle, be interpreted to infer the morphology of the peatland. Here, to some extent, we show how to shortcut the process and include the expectation and uncertainty regarding the peat resistivity directly into a probabilistic inversion workflow. The present approach allows for retrieving what really matters: the spatial distribution of the probability of peat occurrence, rather than the mere electrical resistivity. To evaluate the efficiency and effectiveness of the proposed probabilistic approach, we compare the outcomes against the more traditional deterministic fully nonlinear (Occam's) inversion and against some boreholes available in the investigated area.Wetlands are carbon pools subtracting carbon dioxide from the atmosphere and accumulating it underground. The processes ongoing in these ecosystems form a dark soil, extremely rich in organic matter, known as "peat." Globally, peatlands are responsible for storing almost as much carbon as the atmosphere. For that reason and to preserve them, it is very important to develop tools capable of mapping peatlands. A good candidate for this goal is a geophysical method based on the diffusion of electromagnetic signals into the ground. How the electromagnetic signals propagate depends on the electrical resistivity of the subsurface. So, by studying those signals, we can think of reconstructing, point-by-point, the electrical resistivity variability in the subsurface. Broadly speaking, different resistivities correspond to different sediments. So, in turn, we can use the reconstructed resistivity distribution to map the peat deposits. In this study, we show how to translate directly the electromagnetic signal into the probability of finding peat (rather than in recovering the electrical property of the subsurface). We tested our approach on a data set collected across an Alpine peatland in Italy and we verified our results against direct investigations and against more traditional ways to interpret the geophysical signals.Electromagnetic data can be inverted directly for the probability of peat and clay occurrence The 3D geometry and volume of Alpine peatlands were determined via a fast, proximal-sensing, geophysical method and verified against boreholes Probabilistic approaches to geophysical inversion can incorporate complex prior information and, contextually, assess result uncertainties