Scalable High-Resolution Seismic Tomography

Traditional ray-based tomography aims at recovering the long wavelength of the velocity model, The continuous progress in computing hardware and algorithms allows now for very dense simulation grids. Efficiently targeting finer simulations grids requires strong parallel scalability. High-resolution tomography seems a good candidate to this challenge, either on CPU or on GPU. Tomography workflow is composed of three main steps. The ray-based shooting and the Fréchet derivatives computations steps are embarrassingly parallel although highly imbalanced. The minimization problem step is approximated by solving a linear system with a very sparse and highly rectangular matrix. A suitable parallel implementation on CPU is a client/server paradigm upon dynamic scheduled OpenMP and the modern pipe-l-cg iterative method. Benchmarks performed on Pangea2 supercomputer demonstrated the very well strong scalability behaviour. Furthermore, the move towards GPU is under investigation using streams upon OpenACC for shooting and derivatives processes, and the PETSc-GPU version for the solving step. Ray-based tomography is now capable to preserve and improve the high-frequency content of input velocity model. Moreover, its reasonable computational cost makes it competitive against more intensive computational methods like Full Waveform Inversion.