## 1 Introduction  
Cast blasting is crucial in enhancing stripping efficiency [1, 2, 3] and reducing production costs [4, 5, 6]. During the open-pit mining process, due to the layout of the mining system and the spatiotemporal coordination of stripping operations, thin coal seams are often blasted together with rock and discarded as waste, resulting in significant coal resource loss. Experimental studies on optimizing the explosive charge structure for coal-rock mixed blasting under deep-hole bench conditions have been conducted to improve the recovery rate of thin coal seams and enhance coal resource extraction efficiency. They propose a rational charge structure to facilitate the mixed blasting of coal and rock, achieving selective mining. Advancing the development of deep-hole bench coal-rock mixed blasting selective mining technology is of substantial practical importance for enabling economic, efficient, and environmentally friendly mining. In recent years, significant advancements have been made in blasting theory research and practical exploration, including throw blasting theory analysis and methods such as blast effect control and prediction. For instance, [PERSON] et al. [6] employed regression analysis and other techniques to develop a model that related throw blasting bench parameters to stripping costs and introduced a multi-objective optimization model-solving method based on the non-dominated sorting genetic algorithm (NSGA-II) with an elitism strategy. [PERSON] et al. [7] applied the factor analysis method (FAM) to develop a correlation analysis model for the factors affecting throw blasting results and derived the calculation formulas for the limit charge coefficient and blast hole spacing. [PERSON] et al. [8] developed calculation methods for the casting velocity and energy of debris in two blasting modes: spherical charge and cylindrical charge. An in-depth analysis was conducted on the impact of key factors such as borehole layout (burden) [9; 10], borehole spacing [10], specific charge [11], delay time [12; 13], and stripping work line length [14] on the throw effect, improving both throw blasting results and mining efficiency to optimize the throw blasting effect. In addition, with the rapid advancement of computers, machine learning theory has been extensively applied to the study of throw blasting pile morphology prediction. For example, the BP neural network prediction model [15], the predictive model based on the combined application of the ELM neural network and the Weibull function [16], the predictive model integrating the genetic algorithm and extreme learning machine (GALEM) [17], the HHO-LSSVM predictive model based on the Gaussian distribution [18], and other methods [19] have been extensively researched and successfully implemented, thereby ensuring the reliability and accuracy of throw blasting parameter design. However, the charge structure is key in adjusting the explosive energy distribution and controlling the blasting outcomes [20; 21]. Optimizing the radial or axial decoupled charge structure is essential for improving the blasting effect and controlling blasting hazards by controlling the explosive distribution density in the borehole and changing the filling or spacing media [22; 23]. For radial decoupled charge blasting, the borehole wall's stress intensity and damage characteristics are primarily related to the gap size between the borehole and the explosive (decoupling coefficient) and the decoupling medium [24; 25]. For example, [PERSON] et al. [26], [PERSON] et al. [27], and [PERSON] et al. [28] compared the blasting effects of decoupled charge structures to air, water, sand, and clay as coupling media through theoretical, numerical, and experimental analyses. The results indicated that decoupled charge blasting with water as the filling medium has higher energy transfer efficiency, contributing to better blasting effects. In addition, [PERSON] et al.