Stochastic analysis of micro-cone penetration tests in snow

Abstract

Cone penetration tests have long been used to characterize snowpack stratigraphy. With the development of sophisticated digital penetrometers such as the SnowMicroPen, vertical profiles of snow hardness can now be measured at a spatial resolution of a few micrometers. By using small penetrometer tips at this high vertical resolution, further details of the penetration process are resolved, leading to many more stochastic signals. An accurate interpretation of these signals regarding snow characteristics requires advanced data analysis. Here, the failure of ice connections and the pushing aside of separated snow grains during cone penetration lead to a combination of (a) diffusive noise, as in Brownian motion, and (b) jumpy noise, as proposed by previous dedicated inversion methods. The determination of the Kramers–Moyal coefficients enables differentiating between diffusive and jumpy behaviors and determining the functional resistance dependencies of these stochastic contributions. We show how different snow types can be characterized by this combination of highly resolved measurements and data analysis methods. In particular, we show that denser snow structures exhibited a more collective diffusive behavior supposedly related to the pushing aside of separated snow grains. On less dense structures with larger pore space, the measured hardness profile appeared to be characterized by stronger jump noise, which we interpret as related to breaking of a single cohesive bond. The proposed methodology provides new insights into the characterization of the snowpack stratigraphy with micro-cone penetration tests.