SUMMARY Application of multicomponent seismic exploration produces multiple images of the same subsurface. For a successful interpretation of multicomponent images, it is crucially important to register them in the same coordinate frame....
moreSUMMARY Application of multicomponent seismic exploration produces multiple images of the same subsurface. For a successful interpretation of multicomponent images, it is crucially important to register them in the same coordinate frame. Accurate registration of time-domain images also provides an effective estimate of the interval VP/VS ratio, a major petrophysical attribute. We propose a multistep approach to image registration, which consists of initial interpretation, amplitude and frequency balancing, registration scan, and ...
Morphological component analysis (MCA) is a powerful tool used in image processing to separate different geometrical components (cartoons and textures, curves and points etc). MCA is based on the observation that many complex signals may...
moreMorphological component analysis (MCA) is a powerful tool used in image processing to separate different geometrical components (cartoons and textures, curves and points etc). MCA is based on the observation that many complex signals may not be sparsely represented using only one dictionary/transform, however can have sparse representation by combining several over-complete dictionaries/transforms. In this paper we propose seislet-based MCA for seismic data processing. MCA algorithm is reformulated in the shaping-regularization framework. Successful seislet-based MCA depends on reliable slope estimation of seismic events, which is done by plane-wave destruction (PWD) filters. An exponential shrinkage operator unifies many existing thresholding operators and is adopted in scale-dependent shaping regularization to promote sparsity. Numerical examples demonstrate a superior performance of the proposed exponential shrinkage operator and the potential of seislet-based MCA in application to trace interpolation and multiple removal.
We describe asymptotic analysis of true amplitude DMO processing and of an offset continuation partial differential equation. They are equivalent at offset zero in However, the OC result is true for all and does not depend on the...
moreWe describe asymptotic analysis of true amplitude DMO processing and of an offset continuation partial differential equation. They are equivalent at offset zero in However, the OC result is true for all and does not depend on the assumption. We further show how the output of the DMO processing can be used for AVO/AVA
