Implementation strategies of the seismic Full Waveform Inversion

Full waveform inversion (FWI) is a state-of-the-art method that has been used to estimate parameters of the Earth's subsurface. One of the main drawbacks of the FWI method is its high computational complexity in terms of both time and memory. This occurs because the inversion method is based on the computation of a gradient function that requires the forward (and backward) wave propagation of sources (and residuals) through the subsurface medium. Nowdays, seismic surveys are large scale problems and therefore the computation and storage in RAM memory of both wavefields is not feasible. Therefore, different strategies for the FWI method should be used. In this paper, we use two different implementation strategies that avoid allocating the full wavefields in RAMmemory. Instead, the wavefields are re-computed while at the same time the gradient function is obtained. The recomputation of the wavefields remains possible from a practical point of view since we use parallel architectures (GPUs). We show that the estimated velocity models obtained with all the strategies are similar. We also show that the RAM consumption decreases up to 80% for the proposed strategies in comparison with the strategy that requires storing the full wavefields.