# Research on the Prediction Model of Blasting Vibration Velocity in the Dahuangshan Mine
## 1 Introduction  
Blasting plays a key role in open-pit mining [1], especially in construction stone mines, which account for a large share of open-pit mining. Although blasting is widely used for rock crushing in open pit mines, the impact of blasting vibration cannot be ignored [2]. Sometimes, it is even necessary to use mechanical crushing to eliminate the negative impact of blasting vibration, so the prediction of blasting vibration velocity is particularly critical. The blasting vibration velocity prediction mainly refers to predicting the PPV (peak particle velocity). Blasting workers in open-pit mines often use blasting vibration monitoring results to optimize blasting parameters. In this way, blasting workers can reduce the impact of structures below the steep slope by controlling the PPV. In engineering blasting, people pay more and more attention to avoid serious accidents through conducting blasting vibration prediction in advance, which can help reduce damage to structures and improve personnel safety [3; 4; 5]. Many scholars have conducted extensive and in-depth research on blasting vibration prediction, and their research methods include empirical formulas, machine learning, and numerical simulations. The ground vibration level is described by empirical formulas mainly considering rock characteristics, distance, maximum single-shot charge, and other blasting conditions [6; 7; 8]. The improved formula of the traditional Sadovskii formula is well applied. [PERSON] et al. [9] used the dimensionless analysis method to improve the nonlinear regression prediction model of the blasting vibration propagation law. After the improvement, the model can reflect the elevation effect. According to the measured data, the prediction accuracy of the improved model is improved by 13.55% compared with that of the original model. Machine learning methods have been introduced into the field of blasting vibration prediction, and many scholars have conducted in-depth research on it. [PERSON] et al. [10] established a least squares support vector machine model to predict the blasting vibration effect of open pit mines and optimized the regularization parameters and kernel function width coefficients through a particle swarm optimization algorithm, so that the model has a higher generalization ability and prediction accuracy. [PERSON] et al. [11] considered eight factors, including blast center distance, elevation difference, propagation medium conditions, maximum single-stage charge, total charge, differential time, detonation direction, and the shock absorption effect of the excavated chamber, and established the PSO-LSSVM model to predict the blasting vibration of underground gas storage caverns. YAN et al. [12] used the GEO-ELM model to predict the PPV and frequency of the ground vibration when, caused by blasting demolition, the building collapses. The model has a higher prediction accuracy after combining it with the gray wolf algorithm. The random forest algorithm is widely used in the blasting vibration problem, and the improved method for its algorithm has been proven to have a higher accuracy [13; 14]. The low cost of the numerical simulation method can provide a certain reference for blasting vibration safety evaluation and the optimization of blasting parameters. [PERSON] et al. [15] compared [PERSON]\({}^{\prime}\)s function solution, the experimental results, and the numerical simulations for the PPV results of rock blasting, and the results show that the three are highly consistent. Aiming at the problem of the demolition of adjacent buildings by bench blasting construction, [PERSON] et al. [16] took a bench blasting project as the background and studied the blasting vibration propagation law with a maximum single-shot charge of 3.8 kg and a blast hole diameter of 38 mm under the combined action of horizontal distance and elevation. The blasting vibration velocity is affected by many aspects in the open-pit mine [17; 18; 19], which affects the accuracy of the blasting vibration velocity prediction.