Acoustic and Elastic Full Waveform Tomography

Abstract

For a better estimation of subsurface parameters we develop imaging methods that can exploit the richness of full seismic waveforms. Full waveform tomography (FWT) is a powerful imaging method and emerges as an important procedure in hydrocarbon exploration and underground construction. It is able to recover high-resolution multi-parameter subsurface images from recorded seismic data. For the reconstruction of 3D subsurface structures we apply large-scale 3D elastic and acoustic FWT, which require extensive optimization of runtime and performance. For an improvement of the gradient based optimization method we apply the inverse of a diagonal Hessian approximation for preconditioning of the gradients. However, its calculation is computationally expensive, as it requires one additional forward simulation for each receiver of the underlying seismic acquisition geometry. Therefore, we calculate it only once for an iteration subset of each stage of the multi-stage workflow considering several frequency ranges. The performance is shown for a simple transmission geometry application. Source and receiver artifacts were removed sufficiently and the inversion was successfully performed. The second application shows the effects of the number of sources – correlating with the number of simulations and, thus, mainly affecting the computational efforts – on the reconstruction of a 3D acoustic model in reflection geometry. To allow a successful inversion of the 3D structures and to avoid artifacts due to spatial aliasing, a reasonable number of sources is required. This essential amount of sources depends on the choice of seismic frequencies. Thus, we recommend to reduce simulations at early FWT stages (low frequencies) and to increase the number of sources with increasing frequencies.