A new paper is added to the collection of reproducible documents:
Automated spectral recomposition with application in stratigraphic interpretation

Analyzing seismic attributes in the frequency domain is helpful for reservoir characterization. To analyze the reservoir interval of interest in detail, it is important to capture the seismic response at each frequency subset. Spectral recomposition can be used to extract significant components from the seismic spectrum. We propose a separable nonlinear least-squares algorithm for spectral recomposition, which estimates both linear and nonlinear parts automatically in separate steps. Our approach is applied to estimate fundamental signal parameters, peak frequencies and amplitudes, with which the seismic spectrum can be reconstructed. Automated spectral recomposition helps us visualize frequency-dependent geological features on both cross sections and time slices by extracting significant frequency components. Spectral recomposition can also indicate how frequency contents attenuate with time.

A new paper is added to the collection of reproducible documents:
Omnidirectional plane-wave destruction

Steep structures in seismic data may bring directional aliasing, thus plane-wave destruction (PWD) filter can not obtain an accurate dip estimation. We propose to interpret plane-wave construction (PWC) filter as a line-interpolating operator and introduce a novel circle-interpolating model. The circle-interpolating PWC can avoid phase-wrapping problems, and the related circle-interpolating plane-wave destruction (PWD) can avoid aliasing problems. We design a 2D maxflat fractional delay filter to implement the circle interpolation, and prove that the 2D maxflat filter is separable in each direction. Using the maxflat fractional delay filter in the circle interpolation, we propose the omnidirectional plane-wave destruction (OPWD). The omnidirectional PWD can handle both vertical and horizontal structures. With a synthetic example, we show how to obtain an omnidirectional dip estimation using the proposed omnidirectional PWD. An application of the omnidirectional PWD to a field dataset improves the results of predictive painting and event picking, as compared to conventional PWD.