Research on Magnetic Anomaly Data Processing Based on Wavelet Threshold and EEMD

Abstract

Magnetic field signal can spread stably in different media, which has outstanding advantages in magnetic target detection. However, the magnetic anomaly signal caused by magnetic target are easy to be interfered by the background magnetic field, it is difficult to distinguish magnetic anomalies from the signal, so it is necessary to process the magnetic anomaly data before identification. In this paper, the problem of low signal-to-noise ratio (SNR) of magnetic anomaly signal is discussed and ensemble empirical mode decomposition (EEMD) and wavelet decomposition (WT) are used to process the simulated magnetic anomaly signal. Firstly, EEMD is used to process the simulated magnetic anomaly signal. Then the decomposed high-order IMF components are denoised by wavelet threshold denoising. The results show that compared with using wavelet threshold or EEMD to simulated signal alone, this hybrid method can get data which is more stable and have a taller SNR.