Random and coherent noise attenuation by empirical mode decomposition

Abstract We have devised a new filtering technique for random and coherent noise attenuation in seismic data by applying empirical mode decomposition (EMD) on constant-frequency slices in the frequency-offset (f-x) domain and removing the first intrinsic mode function. The motivation behind this development is to overcome the potential low performance of f-x deconvolution for signal-to-noise enhancement when processing highly complex geologic sections, data acquired using irregular trace spacing, and/or data contaminated with steeply dipping coherent noise. The resulting f-x EMD method is equivalent to an autoadaptive f-k filter with a frequency-dependent, high-wavenumber cut filtering property. Removing both random and steeply dipping coherent noise in either prestack or stacked/migrated sections is useful and compares well with other noise-reduction methods, such as f-x deconvolution, median filtering, and local singular value decomposition. In its simplest implementation, f-x EMD is parameter-free and can be applied to entire data sets without user interaction.