Two-dimensional frequency-domain visco-elastic full waveform inversion: Parallel algorithms, optimization and performance

Full waveform inversion (FWI) is an appealing seismic data-fitting procedure for the derivation of high-resolution quantitative models of the subsurface at various scales. Full modelling and inversion of viscoelastic waves from multiple seismic sources allow for the recovering of different physical parameters, although they remain computationally challenging tasks. An efficient massively parallel, frequency-domain FWI algorithm is implemented here on large-scale distributed-memory platforms for imaging two-dimensional visco-elastic media. The resolution of the elastodynamic equations, as the forward problem of the inversion, is performed in the frequency domain on unstructured triangular meshes, using a low-order finite element discontinuous Galerkin method. The linear system resulting from discretization of the forward problem is solved with a parallel direct solver. The inverse problem, which is presented as a non-linear local optimization problem, is solved in parallel with a quasi-Newton method, and this allows for reliable estimation of multiple classes of visco-elastic parameters. Two levels of parallelism are implemented in the algorithm, based on message passing interfaces and multi-threading, for optimal use of computational time and the core-memory resources available on modern distributed-memory multi-core computational platforms. The algorithm allows for imaging of realistic targets at various scales, ranging from near-surface geotechnic applications to crustal-scale exploration. (C) 2010 Elsevier Ltd. All rights reserved.