## 1 Introduction  
With the rapid development of underground spaces for energy storage [1], transport [2] and mining [3], the surrounding rock stability is increasingly significant for the safe operation of underground caverns. Rock formation can fundamentally determine the geological strength [4], as cracked surrounding rocks risk collapse and require rock reinforcement approaches. To visibly evaluate the surrounding rock quality, opening and pit sampling approaches (e.g., [5, 6]) are extensively utilized and can accurately quantify the attribute parameters. However, intermittent sampling fails to detect the entire rock section continuously, while mechanically opening the rocks is destructive and time-consuming. Some researches (e.g., [7, 8]) established sensor arrays for stress-strain monitoring, but the detection positions were discontinuously distributed inside the shallow ground. Therefore, developing a field-efficacious non-destructive detection technology to scan the surrounding rock profiles continuously is significant for rock structural stability. Ground penetrating radar (GPR) is a remote sensing technology originally designed to detect the ice thickness while primarily applied in discovering the unseen objects beneath the earth's surface, e.g., with applications in mapping subsurface stratigraphy [9], archaeological prospection [10] and detecting pavement defects [11]. Since GPR works as an echo-listener recording electromagnetic waves reflected from interfaces with dielectric differences, this non-destructive technology has the potential to distinguish fresh and weathered rock masses. However, signal interference is an unavoidable issue affecting radargram interpretation. Inversion and migration approaches (e.g., [12; 13]) were developed based on Maxwell's equations to eliminate signal interference, but excessive derivative and integral calculations are time-consuming. Machine learning (e.g., [14; 15]) provides automatic approaches for target identification from radargrams, but required considerable datasets with similar underground attributes are difficult to acquire from field measurement. [PERSON] and [PERSON] [16] developed a discrete grid method to eliminate hyperbolic interference in rock detection, but with many parameters determined by experience. Since electromagnetic waves would be reflected and interfered frequently in cracked rock sections, a novel approach is required to improve efficiency in handling signal interference. Modal analysis is a common signal analysis approach in structural health monitoring. Generally, it characterizes the vibration signals in frequency and time domains, which provide attribute and stability information of the tested structure. The modal difference exists between intact and cracked rock masses, and GPR can capture this subterranean structural feature. Different from classic approaches that concern the physics of wave propagation for GPR signal processing, modal analysis methods consider the physics of signal strength and frequencies. Some researches have attempted to interpret radargrams with these methods, e.g., wavelet transform [17; 18] and empirical mode decomposition [19; 20], but their applications are either at the de-noise stage or in simple object identification. To our best knowledge, no research has modally analyzed GPR radargrams with complicated targets like cracked rock masses. In the cracked subsurface area, numerous rock fissures are nearly continuously distributed, therefore instantaneous changes of GPR signals becoming significant in modal analysis. Empirical mode decomposition (EMD) was originally proposed by [PERSON] et al. [21] for nonlinear and non-stationary signal analysis and then extensively applied to extract modes from signals (e.g., [22; 23]). It can decompose signals into multiple intrinsic mode functions (IMFs) which focus on the local time scale and contain instantaneous frequency information, thereby having the potential to analyze instantaneous changes in radargrams.