Signal enhancement of GPR data based on empirical mode decomposition

Abstract

In GPR data processing, it is an important task to find the reflections obscured by the noise. The `empirical mode decomposition' (EMD) method, the key part of Hilbert - Huang transform (HHT), has been used widely to analyze nonlinear and non-stationary data. This paper uses the ensemble EMD (EEMD) combined instantaneous analysis to remove the noise from GPR data. Some obscured reflections are shown in IMFs after decomposition by EEMD. After removing the high frequency noise, the reconstructed profile is obtained. Instead of applying the instantaneous analysis to the reconstructed data directly, the instantaneous attributes are obtained from the differentiated data. This extra step improves the signal resolution. The field data processing results show that the obscured targets in the raw data can be identified clearly. The processing used in this paper can improve data interpretation in GPR detection.