doi:10.1016/j.pepi.2007.06.008
Copyright © 2007 Elsevier B.V. All rights reserved.
Parallel computing of multi-scale continental deformation in the Western United States: Preliminary results
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Mian Liua, Youqing Yanga, 
, 
, Qingsong Lib and Huai Zhanga, c
aDepartment of Geological Sciences, University of Missouri-Columbia, Columbia, MO 65211, USA
bLunar and Planetary Institute, Houston 77058, USA
cComputational Geodynamics Lab, Graduate University of Chinese Academy of Sciences, Beijing, China
Received 18 January 2007;
revised 16 June 2007;
accepted 16 June 2007.
Available online 1 July 2007.
Abstract
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 arise 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. The heterogeneous rheology and multiple driving forces cause continents to deform over different spatiotemporal scales with different physical processes, yet most geodynamic models for continental tectonic avoid dealing with such multiphysics partly because of (1) the limited observational constraints of lithospheric structure and deformation, and (2) high demands on computing algorithms and resources. These 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 subcontinental-scale model, we used fine numerical meshes to incorporate all major tectonic boundaries and rheological heterogeneities in the model to explore their interplay with tectonic driving forces in controlling active tectonics in the western US. In another model for the entire San Andreas Fault system, we explored strain localization and simulated fault behavior at multi-timescales ranging from rupture in seconds to secular fault creep in tens of thousands of years. These models can help to integrate data grids with distributed high-performance computing resources in the emerging geosciences cyberinfrastructure.
Keywords: Parallel computing; Continental tectonics; Finite elements; San Andreas Fault; Western US; Cyberinfrastructure
Fig. 1. Seismicity and tectonics provinces of the western United States. Epicenter data (red dots) are for 1973–2002 earthquakes (USGS). The simplified boundaries of tectonic provinces are used in the subcontinental model (see Fig. 2). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
Fig. 2. The 3D finite element model for active tectonics in the western US. The model has 371260 FE nodes and 693522 triangle prism elements. The vertical extension is 200 (km), divided into 18 layers. Boundaries of major tectonic units are included, so is the topography as shown in the figure with vertical exaggeration. Boundary conditions and other parameters are given in the text.
Fig. 3. Color-filled contours of the predicted vertical stress, which is largely dependent on topographic loading. The results demonstrate the fine spatial resolution of the 3D model.
Fig. 4. The predicted maximum shear stress (background color) and stress states (shown as lower hemisphere stenographic projections) in the upper crust resulting solely from excess gravitational potential energy. See text for details.
Fig. 5. Predicted surface velocity (curves) driven solely by gravitational spreading. The values are along a profile from (230°, 40°) to (270°, 36°). The model assumes a homogeneous viscosity, and the values are from models with different viscosities: purple: 1023 Pa s; blue: 2 × 1022 Pa s, and green: 5 × 1021 Pa s. The dots with error bars are the GPS data from a 2° swath projected onto the profile. SAF: the location of the San Andreas Fault. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
Fig. 6. Predicted surface velocity (curves) driven solely by the plate boundary forces, which in the model varies with the viscosity of the plate boundaries. Blue curves: 1021 Pa s; purple curves: 1018 Pa s. See Fig. 5 for the location of the profile and the GPS data (dots with error bars). The green curves results of a model with heterogeneous rheological structure that includes the Eastern California Shear Zone and other major faults as weak zones. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
Fig. 7. Comparison of the predicted surface velocities (black) with the averaged GPS velocities (red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
Fig. 8. Comparison of the predicted directions of the maximum horizontal compressive stresses (black) with those from the World Stress Map (red). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
Fig. 9. Predicted strain energy in the upper crust. Grey dots are epicenters of earthquakes with magnitude ≥3.5 from 1973 to 2006 (data from NEIC catalog).
Fig. 10. Topographic relief and seismicity in California and surrounding regions. Data of seismicity (includes M > 5.0 earthquakes from 1800 to present) are from the NEIC catalog.
Fig. 11. Numerical mesh and boundary conditions of the finite element model for the SAF system. The entire San Andreas Fault (black line) is explicitly included in the model.
Fig. 12. Predicted long-term slip rates (numbers beside the fault) along the San Andreas Fault. Numbers in parenthesis are geological slip rates from the California Geological Survey (http://www.consrv.ca.gov/CGS/rghm/psha/index.htm).
Fig. 13. The predicted plastic energy release off the SAF main trace, vertically integrated through the upper crust. The areas of high-energy release coincide with many active faults in California, including the Maacama-Garberville Fault (MGF), the Rodgers Creek Fault (RC), the Hayward Fault (HF), the Calaveras Fault (CF), the Garlock Fault (GF), the East California Shear Zone, the San Jacinto Fault (SJF), the Elsinore Fault (EF), the Palos Verdes Fault (PVF), and the Coronado Bank Fault (CBF). Cycles are seismicity from 1800 to 2004 (NEIC).
Fig. 14. Predicted long-term surface velocities (arrows) and GPS velocities (arrows with confidence ellipses). The GPS data are from USGS and the SCEC Crustal Motion Map Version 3.0 (http://epicenter.usc.edu/cmm3/). AA′ shows the location of the profile in Fig. 16.
Fig. 15. Predicted short-term surface velocities (arrows) and GPS velocities (arrows with confidence ellipses).
Fig. 16. Comparison of the predicted long- and short-term surface velocity with the GPS data along AA′ profile shown in Fig. 14 and Fig. 15.
Fig. 17. Stress evolution at three sample points in the central segment of the SAF.
Fig. 18. Stress evolution of groups of sample points at central SAF (a) and southern SAF (b).
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