Seismic full-waveform inversion tries to estimate subsurface medium parameters from seismic data. Areas with subsurface salt bodies are of particular interest because they often have hydrocarbon reservoirs on their sides or underneath. Accurate reconstruction of their geometry is a challenge for current techniques. This paper presents a parametric level-set method for the reconstruction of salt-bodies in seismic full-waveform inversion. We split the subsurface model in two parts: a background velocity model and a salt body with known velocity but undetermined shape. The salt geometry is represented by a level-set function that evolves during the inversion. We choose radial basis functions to represent the level-set function, leading to an optimization problem with a modest number of parameters. A common problem with level-set methods is to fine-tune the width of the level-set boundary for optimal sensitivity. We propose a robust algorithm that dynamically adapts the width of the level-set boundary to ensure faster convergence. Tests on a suite of idealized salt geometries show that the proposed method is stable against a modest amount of noise. We also extend the method to joint inversion of both the background velocity model and the salt geometry.@en

Full-waveform inversion attempts to estimate a high-resolution model of the Earth by inverting all the seismic data. This procedure fails if the Earth model contains high-contrast bodies such as salt and if su ciently low frequencies are absent from the data. Salt bodies are important for hydrocarbon exploration because oil or gas reservoirs are often located on their sides or underneath. We represent the shape of the salt body with a level set, constructed from radial basis functions to keep its dimensionality low. We have shown earlier that the salt body can be completely recovered if the sediment structure is already known. In this paper, we propose a strategy to simultaneously reconstruct the sediment and the salt. The sediment is implicitly represented by a bilinear interpolation kernel with a small number of variables. An alternating minimization technique solves the resulting optimization problem. The results on a synthetic model using Gauss-Newton approximation of the Hessian shows the feasibility of the approach@en

Seismic full-waveform inversion has become a popular technique for imaging the subsurface from seismic data. The reconstruction of the subsurface medium parameters becomes challenging in the presence of sharp contrasts such as salt bodies. We address the problem by splitting the subsurface model in two parts: a background velocity model and a salt body with known constant velocity but undetermined shape. The salt geometry is represented by a level-set function parametrized by radial basis functions. This leads to a non-linear optimization problem with a modest number of parameters. We improve the convergence speed by utilizing second-order updates from Gauss-Newton Hessian. Tests on the suite of idealized salt models show that the proposed method accurately determines the salt geometry in the presence of a modest amount of noise.@en

In seismic exploration, the delineation of large bodies with hard exterior contrasts but nearly constant interior properties is a challenge. Examples include salt diapirs, salt slabs, anhydrite or basalt layers. Salt geometries are of particular interest because they often have hydrocarbon reservoirs on their sides or underneath. This papers introduces a parametric level-set method for the reconstruction of such geometries in seismic full-waveform inversion (FWI). The level-set determines the outline of the salt geometry and evolves during the inversion in terms of its underlying parameters. For the latter, we employ Gaussian radial basis functions that can represent a large class of shapes with a small number of parameters. This keeps the dimensionality of the inverse problem small, which makes it easier to solve. First tests on a simple 2-D square box model show dramatic improvements over classic FWI.@en