MUSE: Modeling Uncertainty as a Support for Environment

To fully understand a Natural System, the representation of an environmental variable's distribution in 3D space is a mandatoryand complex task. The challenge derives from a scarcity of samples number in the survey domain (e.g., logs in a reservoir, soilsamples, fixed acquisition sampling stations) or an implicit difficulty in the in-situ measurement of parameters. Field or labmeasurements are generally considered error-free, although not so. That aspect, combined with conceptual and numericalapproximations used to model phenomena, makes the results intrinsically less performing, fading the interpretation.In this context, we design a computational infrastructure to evaluate spatial uncertainty in a multi-scenario application in En-vironment survey and protection, such as in environmental geochemistry, coastal oceanography, or infrastructure engineering.Our Research aims to expand the operative knowledge by developing an open-source stochastic tool, named MUSE, the acronymfor Modeling Uncertainty as a Support for Environment. At this stage, the methodology mainly includes the definition of aflexible environmental data format, a geometry processing module to discretize the space, and geostatistics tools to evaluatethe spatial continuity of sampled parameters, predicting random variable distribution. The implementation of the uncertaintymodule and the development of a graphic interface for ad-hoc visualization will be integrated as the next step. The postersummarizes research purposes, and MUSE computational code structure developed so far.