Published as Geophysics, 84, S187–S200, (2019)
Least-squares path-summation diffraction imaging using sparsity constraints
Mrinal K. Sen
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 propose 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. Both 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 effectivness of the proposed method.