Simon Luo

A Deconvolution-based Objective Function For Wave-equation Inversion

We propose a new objective function for wave-equation inversion that seeks to minimize the norm of the weighted deconvolution between synthetic and observed data. Compared to more the conventional difference-based objective function which minimizes the norm of the residual between synthetic and observed data, the deconvolution-based objective function is less susceptible to cycle skipping and local minima. Compared to a crosscorrelation-based objective function, the deconvolution-based objective function is less sensitive to a bandlimited or non-impulsive source function, which may result in a nonzero gradient of the objective function even when the constructed velocity model matches the true model.

Non‐vertical deformations for seismic image flattening

Seismic image flattening produces subsurface images in which sedimentary layering is horizontal. With flattened images, interpretation of stratigraphic features is straightforward, and horizon picking is trivial. Most flattening methods are limited to vertical shearing and stretching of an image. Because of this limitation, these methods may have difficulty flattening seismic images that contain non-vertical deformations without significantly distorting image features. We propose a new image flattening method that uses a vector shift field, instead of a scalar field of vertical shifts, to represent deformation in an image. The method can flatten by vertically shearing or by rotating portions of an image, or by a combination of vertical shear and rotation. Because it is not limited to vertical shearing, the method can flatten in ways more consistent with geologic deformation.