Application of Borehole Radar Data Processing Based on Empirical Mode Decomposition

摘要

Borehole Radar (BHR) uses ultra-wideband electromagnetic (EM) waves to image discontinuities in formations. It has been a major bottleneck to extend BHR applications to obtain a clear and high-resolution radar profile in a complex and noisy environment, which increases ambiguity in the geology interpretation. To avoid this increased ambiguity in the geology interpretation, we proposed a scheme based on the empirical mode decomposition (EMD) and complex signal analysis theory to process the BHR data with low signal to noise ratio (SNR). The scheme includes four steps. First, the original radar profile is pre-processed to avoid mode confusion and noise interference to the radar echo. Next, the EMD method is used to process a single-channel radar dataset and to analyze the frequency components of the radar signal. Various intrinsic modes of the pre-processing radar profile are also obtained by using EMD. Finally, we reconstruct the intrinsic mode profile, which contains information about the formation, calculate the complex signals of the reconstructed radar profile using the Hilbert transform, extract the three instantaneous attributes (instantaneous amplitude, instantaneous phase, and instantaneous frequency), and draw the separate instantaneous attributes profiles. This processing scheme provides both the conventional time-distance profile also in addition to the three instantaneous attributes. The additional attributes reduce ambiguity when evaluating the original radar profile and avoid the deviation relying solely on a conventional time-distance profile. An actual radar profile, which was obtained by a BHR system in a limestone fracture zone, is used to verify the effectiveness of instantaneous attributes for improving interpretation accuracy. The results demonstrate that the EMD method is superior in processing the BHR signal under a low SNR and has the capability to separate the high-low components of the radar echo effectively.