Application of Variational Mode Decomposition in Random Noise Attenuation and Time-frequency Analysis Seismic Data

Abstract

We introduce a novel approach for both random noise attenuation and time-frequency analysis of seismic data. The method is inspired by the newly developed variational mode decomposition (VMD). The principle of VMD is to look for an ensemble of modes with their respective center frequencies, such that the modes collectively reproduce the input signal and each mode is smooth after demodulation into baseband. The advantage of VMD is that there is no residual noise in the modes and it can avoid redundant modes compared with the complete ensemble empirical mode decomposition (CEEMD) method. Moreover, The VMD is an adaptive signal decomposition technique, which can non-recursively decompose a multi-component signal into a number of quasi-orthogonal intrinsic mode functions. This new tool is based on a solid mathematical foundation and can obtain a well-defined time-frequency representation, which is more robust than the empirical mode decomposition (EMD) based decomposition approaches, such as the EEMD and CEEMD, which still remains empirically. We apply the VMD algorithm to random noise attenuation of seismic data by summarizing the low-frequency components after VMD. The application on field data demonstrates the potential of the proposed approach in highlighting geological characteristics (e.g. faults) effectively. All the performances of the VMD based approach are compared with those from the CEEMD based approach.