GSA Connects 2022 meeting in Denver, Colorado

Paper No. 101-13
Presentation Time: 9:00 AM-1:00 PM


BADEN, Curtis1, BÜRGMANN, Roland1, ROUSSET, Baptiste2, CHANARD, Kristel3 and FLEITOUT, Luce4, (1)Dept Earth & Planetary Science, UC Berkeley, Berkeley, CA 94720, (2)Institut Terre & Environnement de Strasbourg, Chargé de recherche CNRS, Strasbourg, 67084, France, (3)Institut de Physique du Globe de Paris, Université de Paris, Paris, 75013, France, (4)Laboratoire de geologie - CNRS UMR 8538, Ecole normale supérieure - PSL University, Paris, 75230, France

The rheology of Earth’s crust and upper mantle influences the distribution and magnitude of deformation at Earth’s surface in response to both tectonic and non-tectonic processes. Existing constraints on Earth’s lithospheric rheology largely arise from either (a) ex situ laboratory measurements of stress and strain rates on rock samples from the lower crust and upper mantle, or (b) inferred constitutive relationships based on observations of ground deformation measured with geodesy. However, laboratory measurements may be inappropriately extrapolated to crustal-scale systems because stresses and strain rates in laboratory experiments and in Earth’s crust differ by many orders of magnitude. Although indirect rheological estimates inferred from geodetically measured ground deformation following large earthquakes are directly applicable to crustal-scale problems, such estimates are sparse in both time and space. Geodetic measurements of hydrologic loading-induced deformation near large lakes, reservoirs, and ice sheets also yield estimates of rheological behavior in Earth’s crust and upper mantle.

Seasonal hydrologic loading signals are widespread over large areas and periodic in time, and therefore particularly advantageous for use in estimating and further constraining lithospheric rheology. In this project, we estimate the rheology and structure of California's crust and upper mantle based on observed phase shifts between seasonal hydrologic loading and surface deformation recorded by stations of the Global Navigation Satellite System (GNSS). We use a 3D finite element model to predict the viscoelastic response due to the annual hydrologic loading in California captured by the sum of groundwater, soil moisture, and surface water due to rain and snow, and artificial lake filling, and compare the surface deformation response for various rheological structures to the annual deformation observed by GNSS. We use this approach to predict new estimates of California’s lower crustal and upper mantle rheology, which we compare to rheological estimates based on geological data and laboratory experiments, and from post-seismic studies and water level variations in large lakes.