Abstract
Full waveform inversion (FWI) is one of the most
challenging procedures to obtain quantitative information
of the subsurface. For elastic inversions, when both com-
pressional and shear velocities have to be inverted, the
algorithmic issue becomes also a computational challenge
due to the high cost related to modelling elastic rather than
acoustic waves. This shortcoming has been moderately mit-
igated by using high-performance computing to accelerate
3D elastic FWI kernels. Nevertheless, there is room in the
FWI workflows for obtaining large speedups at the cost of
proper grid pre-processing and data decimation techniques.
In the present work, we show how by making full use of
frequency-adapted grids, composite shot lists and a novel
dynamic offset control strategy, we can reduce by several
orders of magnitude the compute time while improving the
convergence of the method in the studied cases, regardless
of the forward and adjoint compute kernels used.
challenging procedures to obtain quantitative information
of the subsurface. For elastic inversions, when both com-
pressional and shear velocities have to be inverted, the
algorithmic issue becomes also a computational challenge
due to the high cost related to modelling elastic rather than
acoustic waves. This shortcoming has been moderately mit-
igated by using high-performance computing to accelerate
3D elastic FWI kernels. Nevertheless, there is room in the
FWI workflows for obtaining large speedups at the cost of
proper grid pre-processing and data decimation techniques.
In the present work, we show how by making full use of
frequency-adapted grids, composite shot lists and a novel
dynamic offset control strategy, we can reduce by several
orders of magnitude the compute time while improving the
convergence of the method in the studied cases, regardless
of the forward and adjoint compute kernels used.
Original language | English |
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Article number | 10.1007/s10596-016-9593-0 |
Pages (from-to) | 1-15 |
Number of pages | 15 |
Journal | Computational Geosciences |
Publication status | Published - 22 Oct 2016 |