Global Navigation Satellite Systems (GNSS) have revolutionized the ability to monitor Earth- system processes, such as volcanic and tectonic deformation, with important implications for natural-hazard assessment. To accurately detect signals of interest, however, extraneous noise must be filtered from the time series. Surface mass loading (SML) produces continual deforma- tion of the solid Earth, which manifests ubiquitously in GNSS receiver-position estimates. Thus, neglecting to account for SML in GNSS analyses can significantly inhibit the detection of ground motions caused by subtle Earth-system processes, including aseismic transient deformation at con- vergent plate boundaries. Except for Earth’s response to ocean tidal loading, however, most SML response signals are not routinely removed from GNSS observations. Here, I propose to explore the contributions of oceanic, atmospheric, and hydrologic mass loading to GNSS-inferred surface displacements across Japan, Cascadia, and Alaska. By improving the methods of prediction and empirical estimation of SML-induced deformation, I aim to reduce the variance of GNSS time series and therefore enhance the ability to resolve tectonic processes.
In collaboration with NASA’s Jet Propulsion Laboratory (JPL) and students at the University of Montana, I will assess contributions from individual SML sources to GNSS-inferred receiver po- sitions using time series analysis and Earth-deformation modeling. Empirical data collected by the GEONET system in Japan and the Plate Boundary Observatory in North America will be pro- cessed using JPL’s GIPSY software. Simulated surface displacements will be derived from Earth- response functions and global mass-load models, constrained in part by Earth-system observations from space-based platforms, including satellite altimetry and NASA’s GRACE mission.