Random and coherent noise attenuation by empirical mode decomposition

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.