Random and coherent noise attenuation by empirical mode decomposition
Maiza Bekara, Mirko van der Baan · 2008
This paper proposes a new filtering technique for random and coherent noise attenuation by means of empirical mode decomposition (EMD) in the f-x domain. 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 shown to be equivalent to an auto-adaptive f-k filter with a frequency-dependent, high-cut wavenumber filtering property. It is useful in removing both random and dipping noise in either pre-stack or stacked/migrated sections 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 datasets in an automatic way.