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Geostatistical simulation of subglacial topography


The topography beneath glaciers controls the flow of ice and subglacial water. Traditional approaches for estimating the topography produce topography that is unrealistically smooth, which can bias ice sheet models. We use geostatistical methods to stochastically simulate realistically rough topography. Ongoing research includes the development of geostatistical methods and their application in Greenland and Antarctica.

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Geophysical data analysis and interpretation

Geophysical tools such as ice-penetrating radar, seismology, and satellite altimetry provide valuable information on ice sheet conditions. We analyze this data to characterize the subglacial topography, hydrology, and geology.


GStatSim​ software package

We are continuously working to create open-access Python tools for performing geostatistical interpolation and simulation. Check out our Jupyter Book tutorials here:

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Geophysical inversion​

We combine geostatistics and stochastic modeling with geophysical modeling to perform inversions with a Markov Chain Monte Carlo approach. We are applying these methods to gravity inversions for sub-ice-shelf topography and mass conservation inversions for subglacial topography.

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