Visco-Elastic Wave Propagation on GPUs

With the advent of large offset and low frequency seismic data, the information stored in surveys has become ever richer and more voluminous. At the same time, a push for more detailed solutions requires the inclusion of higher frequencies from the data. Moreover, to support extracting accurate and realistic geophysical models of the subsurface, velocity model building such as done in Full Waveform Inversion (FWI) frequently requires inclusion of anisotropic parameters, elastic, and viscous information. However, the computational cost associated with solving such realistic equations is non-trivial.

In the current work, we have implemented the fully anisotropic viscoelastic equations (and all simpler cases). We use a velocity-stress staggered grid approach proposed in [ 5 ] with optimized FD weights of any spatial order and second order in time. We discuss performance for multi-GPUs & multi-node GPUs, as well as some of the optimization techniques used for convolutional perfectly matched layer (CPML) and MPI.