## 1 Introduction  
Solid rock failure poses a major scientific challenge, constraining the advancement of deep underground infrastructure. Rapid progress in China's large-scale tunnel projects (e.g., the Sichuan-Tibet Railway) [1], the development of the world's energy and the construction of the urban underground, which all have exacerbated high-stress hazards including microearthquakes [2], rockbursts, landslides, and gas outbursts [3]. These disasters not only endanger project safety and stability but also undermine environmental sustainability, human security, and socioeconomic progress. Rock's inherent complexity, diversity, and nonlinearity--key attributes of Earth's crustal materials--give rise to unpredictable and evolving fracture behaviors [4; 5]. Fracture behavior results from the interaction between internal microstructures [6] and external stimuli (e.g., stress, temperature, fluid pressure) [7]. Specific combinations of internal and external factors can trigger rock mass fracturing, leading to catastrophic engineering geological events. On 24 May, 2024, a rockslide in Papua New Guinea killed over 2000 people. Weak rock interfaces and steep slopes synergistically compromised structural integrity, causing collapse. However, uncertainties remain about how stress, deformation, and crack fields evolve in deteriorating discontinuous rock masses [8]. Empirical studies and theoretical models lack sufficient capability to visualize and quantify rock deformation-failure processes [9]. Moreover, the relationships among stress distribution, fracture patterns, and loading conditions are poorly understood [10], hindering accurate disaster assessment and effective mitigation strategies. Therefore, understanding damage evolution and predicting fracture failure are now priorities in rock mechanics [11; 12], offering key insights for improving fracture prediction methodologies [13]. The self-organized criticality (SOC) theory, initially proposed by [PERSON] et al. (1987) [14], elucidates the spontaneous evolution of dissipative systems toward criticality and explains how complex systems self-organize into critical states. However, the pronounced nonlinearities in rock deformation and failure motivate the application of SOC theory to interpret the transition from disordered to ordered internal structures, offering novel insights into fracture propagation in complex nonlinear rock systems [6]. Consequently, SOC theory emerges as a robust framework for investigating rock damage and fracture behaviors. To date, researchers have employed experimental [15], theoretical, numerical [16], and machine learning [12; 17; 18] approaches to investigate self-organized criticality in rock failure and its predictive modeling [19; 20]. Nevertheless, limitations in rock sample mechanical response mechanisms [21; 22] impede comprehensive understanding of rock behavior and accurate prediction of meta-critical phase transitions prior to failure [23]. The intrinsic heterogeneity of rock masses introduces critical uncertainties in progressive damage prediction, which compromises the reliability of conventional detection and characterization methodologies, thereby posing significant challenges to failure forecasting. Real-time CT imaging under variable stress conditions has facilitated visualization of internal failure processes in uniaxial compression [24; 25], loading/unloading cycles [26], triaxial compression [27], and matrix soil loading. Nevertheless, existing methodologies suffer from limited mutual validation across experimental datasets, compromising the reliability of experimental outcomes. Conventional laboratory compression testing and theoretical modeling predominantly rely on empirical dataset representation (e.g., stress-strain curves), but the spatial crack size and localization of the sample cannot be visualized. This visualization deficiency fundamentally limits mechanistic understanding of anisotropic rock material damage propagation.