## 4 Discussion and Conclusions  
Two significant research developments are achieved in this paper: successfully developing mode analysis approach for complicated radargram interpretation, and successfully applying the non-destructive technology GPR for unstable rock structure identification. Since surrounding rock quality attracted attention in underground engineering, standards to evaluate rock quality has continuously evolved, but opening and pit sampling has remained as the field detection strategy yet [36]. Although seismic velocity measurements have been utilized to indirectly estimate rock quality [37], this energy required technology is not suitable in underground caverns. GPR has been applied in object detection, stratum identification and underground water detection, but rarely in such a complicated condition with intensive wave reflection from intensive rock interfaces. Modal analysis has been applied to interpret simple GPR radargrams before, e.g., wavelet transform for de-noising [17] or pipeline identification [18] and EMD for object detection [20], but not in complicated conditions like cracked subsurface rock masses. In this paper, an EEMD-based signal processing approach including three steps, pre-processing, signal decomposition and profile reconstruction, is investigated for interpreting GPR radargrams for unstable subsurface rock structure identification. Amplitude gain function has restored the signals to an analyzable level, against the electromagnetic attenuation which reduces the energy  
Figure 19: IMF2s amplitude profile of the surrounding rock section in Figure 18. Signals with amplitudes less than 500 are removed. Figure 18: (a) Original GPR radargram of another surrounding rock section. (b) Corresponding pre-processed radargram (signals with amplitudes less than 500 are removed). of responses beneath 2 m to 200 times weaker than that in the near-surface layer. The pre-processed radargrams present some different distributions with the original ones, as well as the evidence of intact rock portions (e.g., portion H in Figure 11b). By decomposing the pre-processed GPR signals into multiple IMFs and residues, one typical IMF can preserve the distinct local modes, which arise from instantaneous changes of fissure interfaces, and is chosen to reconstruct the subterranean profile. Different IMFs can present different signal features, including noises, near-surface-layer signals and object responses, while object responses can appear in different IMFs. For example, pipeline responses are preserved in the IMF3 and IMF4 in Figure 4, but only IMF3s profile can accurately show pipeline areas; simulated crack responses are preserved in the IMF6 and IMF7 in Figure 8, but oscillations in the IMF7 have extended outside the cracked portions. Choosing appropriate IMFs to reconstruct the profile is significant for further unstable rock identification. After considering appropriate IMFs to reconstruct the radargram, the intact and cracked rock portions become distinguishable. The developed approach can eliminate the hyperbolic interference signals, but some weak signals remain surrounding the cracked portions in the numerical simulation (Figure 9). Despite these small biases, the identified cracked areas correspond well with the pre-defined ones, and the intact portion G covered by intensive signals become visible in Figure 9. In the field measurements, the intact areas that have shown evidence in pre-processed radargrams are entirely revealed (e.g., I, J and K in Figure 17, P and Q in Figure 19), while those covered by intensive signals in pre-processed radargrams also become visible (e.g., Y in Figure 14, M and N in Figure 17, O in Figure 19). Other portions containing strong responses in the reconstructed radargrams are identified as cracked areas.