HR: 1340h
AN: IN43B-0335    [Abstracts]
TI: Volumetric Rendering of Geophysical Data on Adaptive Wavelet Grid
AU: * Vezolainen, A
EM: a_vez@mail.ru
AF: Florida State University, School of Computational Science Dirac Science Library, Tallahassee, FL 32306 United States
AU: Erlebacher, G
EM: erlebach@csit.fsu.edu
AF: Florida State University, School of Computational Science Dirac Science Library, Tallahassee, FL 32306 United States
AU: Vasilyev, O
EM: oleg.vasilyev@colorado.edu
AF: University of Colorado, Department of Mechanical Engineering, Boulder, CO 80309 United States
AU: Yuen, D A
EM: davey@krissy.msi.umn.edu
AF: University of Minnesota, Dept. Geology and Geophysics and Minnesota Supercomputing Institute, Minneapolis, MN 55455 United States
AB: Numerical modeling of geological phenomena frequently involves processes across a wide range of spatial and temporal scales. In the last several years, transport phenomena governed by the Navier-Stokes equations have been simulated in wavelet space using second generation wavelets [1], and most recently on fully adaptive meshes. Our objective is to visualize this time-dependent data using volume rendering while capitalizing on the available sparse data representation. We present a technique for volumetric ray casting of multi-scale datasets in wavelet space. Rather of working with the wavelets at the finest possible resolution, we perform a partial inverse wavelet transform as a preprocessing step to obtain scaling functions on a uniform grid at a user-prescribed resolution. As a result, a function in physical space is represented by a superposition of scaling functions on a coarse regular grid and wavelets on an adaptive mesh. An efficient and accurate ray casting algorithm is based just on these scaling functions. Additional detail is added during the ray tracing by taking an appropriate number of wavelets into account based on support overlap with the interpolation point, wavelet amplitude, and other characteristics, such as opacity accumulation (front to back ordering) and deviation from frontal viewing direction. Strategies for hardware implementation will be presented if available, inspired by the work in [2]. We will pressent error measures as a function of the number of scaling and wavelet functions used for interpolation. Data from mantle convection will be used to illustrate the method. [1] Vasilyev, O.V. and Bowman, C., Second Generation Wavelet Collocation Method for the Solution of Partial Differential Equations. J. Comp. Phys., 165, pp. 660-693, 2000. [2] Guthe, S., Wand, M., Gonser, J., and Straßer, W. Interactive rendering of large volume data sets. In Proceedings of the Conference on Visualization '02 (Boston, Massachusetts, October 27 - November 01, 2002). Visualization, IEEE Computer Society, Washington, DC, 53-60, 2002.
DE: 0530 Data presentation and visualization
SC: Earth and Space Science Informatics [IN]
MN: Fall Meeting 2005