Adaptive noise suppression for low-S/N microseismic data based on ambient-noise-assisted multivariate empirical mode decomposition - Yu, Zhichao; Huang, Yingkun; Fang, Zisen; Tan, Yuyang; He, Chuan (Frontiers in Physics, 2023)
Microseismic monitoring data may be seriously contaminated by complex and nonstationary interference signals produced by mechanical vibration, which significantly impact the data quality and subsequent data-processing procedure. One challenge in microseismic data processing is separating weak seismic signals from varying noisy data. To address this issue, we proposed an ambient-noise-assisted multivariate empirical mode decomposition (ANA-MEMD) method for adaptively suppressing noise in microseismic data. In the proposed method, a new multi-channel record is produced by combining the low signal-to-noise (S/N) microseismic signal with preceding ambient noises. The multi-channel record is then decomposed using multivariate empirical mode decomposition (MEMD) into multivariate intrinsic mode functions (MIMFs). Finally, the MIMFs corresponding to the main ambient noises can be identified and suppressing by calculating energy percentage in the assisted channels. We investigate the feasibility and reliability of the proposed method using both synthetic data and field data. The results demonstrate that the proposed method can mitigate the mode mixing problem and clarify the main noise contributors by adding additional ambient-noise-assisted channels, hence separating the microseismic signal and ambient noise effectively and enhancing the S/N of microseismic signals.