Large-Scale, Explicit Wave Simulations on the CRAY-2

Abstract

Most time-domain, wave problems in geophysics are intractable by classical analysis methods, due principally to non-separability and to a lesser extent material nonlinearity. Therefore discrete numerical solutions are often necessary for the simulation of realistic models. Applications in 2-D and 3-D geophysical modeling are the subject of this paper, particularly as solved on a Cray-2 supercomputer. Implementation and performance differences between earlier CRAYs and the CRAY-2 are described, including the discrepancy between scalar fetch and vector processing speeds. Explicit finite element solvers are applied to applications involving 2-D simulation of a seismic refraction experiment across the state of Maine, 3-D simulation of near-source scattering experiments, and both linear and nonlinear axisymmetric source simulation. Results show that the CRAY-2 allows cost-effective 2-D simulations of truly large-scale models, but only begins to be effective in 3-D for models of interest in geophysics. The large memory (256 megawords) is adequate but a speed increase of at least an order of magnitude is necessary for cost-effective 3-D. True multiprocessor capability (rather than 'multi-computer') provides an alternative to raw speed but involves another set of difficulties. (Reprints).

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Oct 11, 1988
Accession Number
ADA201531

Entities

People

  • D. K. Vaughan
  • G. L. Wojcik
  • J. Mould
  • M. B. Hulit
  • M. Barenberg

Tags

Communities of Interest

  • Air Platforms
  • Counter IED

DTIC Thesaurus Topics

  • Algorithms
  • Central Processing Units
  • Computer Programming
  • Computers
  • Elastic Waves
  • Explosives
  • Geometry
  • Geophysics
  • Materials
  • Mechanics
  • Models
  • Refraction
  • Scattering
  • Three Dimensional
  • Time Domain
  • Two Dimensional
  • United States

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Parallel and Distributed Computing.
  • Seismology