Adaptive acquisition footprint suppression based on a 3D stationary wavelet transform: A case study from China

To reduce the acquisition footprint in the geometric attribute analysis of marine seismic data, a 3D stationary wavelet transform (SWT) is presented in this paper. We use the 3D SWT to decompose the seismic volume into multiple levels of cubes in three directions, and we determine the different filter parameters, which vary with depth. Then, we implement the suppression of the acquisition footprint by adaptively reconstructing the weighted filtered wavelet components. The field data examples indicate that the method effectively removes the acquisition footprint and suppresses other types of noise while at the same time, preserving and highlighting geological features. The method can improve the accuracy and reliability of geometric attribute analysis (such as eigenstructure-based coherence and 3D volumetric curvature) in characterizing the internal structure of reservoirs and in structural interpretation. The method can be expected to be applicable to the removal of other kinds of noise on marine seismic datasets.

► A 3D stationary wavelet transform-based suppression of acquisition footprint.
► We choose a soft thresholding function to determine the filtering parameters.
► We reconstruct the weighted filtered wavelet components varied with depth.
► The method improves the quality of geometric attribute analysis.