GEO OSE Track 1: Enhancing the accessibility of novel geostatistical inversion workflows for cryosphere research

University Of Florida, Gainesville FL

Investigators

Abstract

Many Earth science data sets require spatial interpolation in order to be used in scientific studies. It is often important that these interpolations are done in a way that satisfies certain physical and statistical properties. These “geostatistical” methods are widely used in the mining and petroleum industries, but there is no free software that makes these methods available to students, academics, and environmental researchers. This project addresses this accessibility barrier by creating freely available geostatistics software. This software will include novel methods for combining physical and statistical information. These algorithms will be fast enough so that they can be used for very large data sets. To make it easy for other researchers in different disciplines to use these tools, the software will be installed in several different online scientific computing hubs. The software will be accompanied by online educational materials. The software will be used to create robust maps of the topography underneath Antarctic glaciers by combining a variety of different measurement sources. These topographic maps will help improve the rigor of ice-sheet and sea level rise models. This work will build upon the existing GStatSim Python geostatistics software and educational Jupyter Book to perform high-speed physics-informed geostatistical simulations. The research team will develop a novel Markov Chain Monte Carlo approach for performing geophysical inversions where geostatistical simulations are iteratively perturbed until the outputs of a geophysical forward model converge with measurements. Parallel processing techniques will be used to improve the scalability of this method. This will enable users to generate ensembles of large-scale geostatistical simulations at high speeds while accounting for both spatial and physical constraints. This method will be tested and applied to two case studies: 1) the simulation of sub-ice-shelf topography using gravity observations, and 2) the simulation of subglacial topography with mass conservation constraints. These case studies will provide critical parameters for ice-sheet models. To facilitate the use of GStatSim in the cryosphere community, these tools will be hosted on the GHub and CryoCloud computing platforms. This software will also be linked to the Earth Science Information Partners toolbox page in order to make this package accessible to broader geosciences audiences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.