Hydrostratigraphical modeling using a 3D geostatistical approach

In hydrogeology and environmental science, it can be important to assess the geological heterogeneity of the subsoil, and to predict or simulate different scenarios of the 3D architecture of the subsoil. The most known methods which predict or simulate categorical data (lithologies) are the indicator based methods (Johnson and Driess, 1989 Trevisani and Fabbri, 2010). Here, the lithological spatial variability is modeled by variograms. Indicator methods consider the categorical nature of lithologies and incorporate the geometric context of geological facies. However, such methods have proven to be difficult in representing and modeling a classical indicator approach, especially when the lithological categories exceed two. In the 1997, Carle and Fogg introduced in geostatistics the transition probability/Markov chain approach (transiograms) where some important geological information are take into account to model spatial variability. The mean lengths of materials, their juxtapositional tendencies and the volumetric proportions are incorporated into the Markov chain model of the transiogram. The spMC package (Sartore, 2013) was developed in R environment with the purpose of analyzing categorical data observed in 3-D locations. Three algorithms to simulate spatial random fields were implemented in the spMC package, including the multinomial categorical simulation (MCS) proposed by Allard et al. (2011). This research concerns an hydrogeological study in an experimental site of 1.5 ha, which is located inside the drinking water supply area of the Padua town (NE, Italy). More specifically, this area is localized inside a plain springs area, in a shallow part of the transition zone between the high and the middle Venetian plain. Here, the results of hydrostratigraphic predictions based on MCS are presented and compared with the classical hydrostratigraphic cross-sections made in this area.