Parallel Computing of Multi-Scale Continental Deformation in the Western United States: Preliminary Results

 

Mian Liu1, Youqing Yang1, Qingsong Li2, and Huai Zhang1,3

 

1 Dept. of Geological Sciences, University of Missouri-Columbia, Columbia, MO 65211, USA

2 Lunar and Planetary Institute, Houston, 77058, USA

3 Computational Geodynamics Lab, Graduate University of Chinese Academy of Sciences, Beijing, China

 

Lithospheric deformation in the western United States is one of the best examples of diffuse continental tectonics that deviate from the plate tectonics paradigm. Conceptually, diffuse continental deformation is known to result from 1) weak and heterogeneous rheology of continents and 2) driving forces that arises from plate boundaries as well as within the continental lithosphere. However, the dynamic interplay of continental rheology and driving forces, hence the geodynamics of continental tectonics, remains poorly understood. Most geodynamic models for continental tectonic avoid dealing with the problems of multiphysics operating over multiple spatiotemporal scales, partly because of 1) the limited observational data of 3D lithospheric structure and stain rates, and 2) high demands on computing algorithms and resources. Theses constraints, however, have relaxed significantly in recent years to permit exploration of some of the multi-scale physics governing continental tectonics. Here we present preliminary results of modeling multi-scale tectonics in the western United States using parallel finite element computation. In a 3D sub-continental scale model, the fine mesh allows us to incorporate all major tectonic boundaries and rheological heterogeneities in the model to explore how they interplay with tectonic driving forces in controlling active tectonics in the western US. We also show a model for the entire San Andreas Fault system to explore strain localization and to simulate fault behavior at multi-timescales ranging from rupture in seconds to secular fault creep in tens of thousands of years. We discuss how these models may contribute to the emerging geosciences cyberinfrastructure that will link data grids with distributed high-performance computing resources.