|Abstract:|| We address the problem of stochastic simulation of soil particle-size curves (PSCs) in heterogeneous aquifer systems. Unlike traditional approaches that focus solely on a few selected features of PSCs (e.g., selected quantiles), our approach is conducive to stochastic realizations of the spatial distribution of the entire particle-size distribution which can optionally be conditioned on available measured data. We
model PSCs as cumulative distribution functions, and their densities as functional compositions in a Bayes Hilbert space. This enables us to employ an appropriate geometry to deal with the data dimensionality and constraints, and to develop a simulation method for particle-size densities (PSDs) based upon a suitable and well defined projection procedure.
The new theoretical framework enables us to represent and reproduce the complete information content embedded in PSC data. As a first field application, we test the quality of unconditional and conditional simulations obtained with our methodology by considering as a test bed a set of particle-size curved collected within a shallow alluvial aquifer in the Neckar river valley, Germany.