HR: 09:45h
AN: S31E-08 INVITED    [Abstracts]
TI: Full waveform seismic inversion: Prospects for scaling to petaflops computers
AU: * Ghattas, O
EM: omar@ices.utexas.edu
AF: The University of Texas at Austin, Jackson School of Geosciences and Institute for Computational Engineering and Sciences, 1 University Station, C0200, Austin, TX 78712, United States
AU: Akcelik, V
EM: volkan@slac.stanford.edu
AF: Stanford Linear Accelerator Center, Advanced Computations Department, SLAC, Menlo Park, CA 94025, United States
AU: Epanomeritakis, I
EM: ioannis.epanomeritakis@gmail.com
AU: Burstedde, C
EM: carsten@ices.utexas.edu
AF: The University of Texas at Austin, Institute for Computational Engineering and Sciences, 1 University Station, C0200, Austin, TX 78712, United States
AU: Bielak, J
EM: jbielak@cmu.edu
AF: Carnegie Mellon University, Department of Civil and Environmental Engineering, Pittsburgh, PA 15213, United States
AB: The U.S., Japanese, and European governments are all pursuing petaflops computing programs, and the first systems capable of a peak petaflops performance are expected to appear in 2008. Problems in the geosciences have been among the important drivers for the development of such systems. One such problem is the seismic inverse problem of determining the distribution of earth properties from surface observations of earthquake-induced high-frequency ground motion in large heterogeneous elastic regions. Scalability of inverse methods on supercomputers requires both algorithmic scalability (scaling to large problems sizes) and parallel scalability (scaling to large numbers of processors). Here we focus on the deterministic inverse problem, and we address both parallel efficiency and algorithmic efficiency of a class of optimization methods for solution of full elastic waveform seismic inverse problems as resolution limits are pushed. We discuss algorithmic choices designed to assure scalability for various components of the inverse method, including inexact Newton for nonlinear iterations, conjugate gradients for linear iterations, primal-dual active sets for treatment of parameter bounds, and primal-dual treatment of total variation regularization. We conclude with an assessment of the prospects of scalability of high-resolution seismic inversion to upcoming petascale computing systems.
DE: 7270 Tomography (6982, 8180)
SC: Seismology [S]
MN: 2007 Fall Meeting