Least-squares path-summation diffraction imaging using sparsity constraints

Abstract

Diffraction imaging aims to emphasize small-scale subsurface heterogeneities, such as faults, pinch-outs, fracture swarms, channels, etc. and can help seismic reservoir characterization. The key step in diffraction imaging workflows is based on the separation procedure suppressing higher energy reflections and emphasizing diffractions, after which diffractions can be imaged independently. Separation results often contain crosstalk between reflections and diffractions and are prone to noise. We have developed an inversion scheme to reduce the crosstalk and denoise diffractions. The scheme decomposes an input full wavefield into three components: reflections, diffractions, and noise. We construct the inverted forward modeling operator as the chain of three operators: Kirchhoff modeling, plane-wave destruction, and path-summation integral filter. Reflections and diffractions have the same modeling operator. Separation of the components is done by shaping regularization. We impose sparsity constraints to extract diffractions, enforce smoothing along dominant local event slopes to restore reflections, and suppress the crosstalk between the components by local signal-and-noise orthogonalization. Synthetic- and field-data examples confirm the effectiveness of the proposed method.