Blasting is an intrinsic component of mining cycle of operation. However, it is usually associated with negative environmental effects such as blast-induced ground vibration (BIGV) which require accurate prediction and control. Therefore, in this study, Gaussian process regression (GPR) has been proposed for prediction of BIGV in terms of peak particle velocity (PPV), while grey-wolf optimization (GWO) algorithm has been used to optimize the blast-design parameters for the control of BIGV in Obajana limestone quarry, Nigeria. The blast-design parameters such as burden (B), spacing (S), hole depth (Hd), stemming length (T), and number of holes (nh) were obtained from the quarry. The distance from the blasting point to the measuring point (D) and the charge per delay (W) were measured and determined, respectively. The PPV was also measured for the number of blasting operations witnessed. These seven parameters were used as inputs to the proposed GPR model, while the PPV was the targeted output. The performance of the proposed model was evaluated using some statistical indices. The output of the GPR model was compared with ANN model and three empirical models, and the GPR model proved to be more accurate with the coefficient of determination (R²) of approximately 1 and variance accounted for VAF of about 100%, respectively. In addition, the GWO was also developed to select the optimum blasting parameters using the ANN model for the generation of objective function. The output of the GWO revealed that if the number of holes (nh) can be reduced by 45% and W by 8%, the PPV will be reduced by about 94%. Hence, the proposed models are both suitable for prediction of PPV and optimization of blast-design parameters.

爆破是采矿作业周期的固有组成部分。但是，它通常与负面的环境影响相关，例如爆炸引起的地面振动（BIGV），需要精确的预测和控制。因此，在这项研究中，提出了高斯过程回归（GPR）来预测峰值粒子速度（PPV）方面的BIGV，而灰狼优化（GWO）算法已用于优化爆炸的设计参数。在尼日利亚Obajana石灰石采石场控制BIGV。从采石场获得了爆破设计参数，例如负荷（B），间距（S），孔深（Hd），茎长（T）和孔数（nh）。爆破点到测量点的距离（D）和每延时电荷（W）分别进行测量和确定。还对PPV进行了见证的爆破操作数量的测量。这七个参数用作建议的GPR模型的输入，而PPV是目标输出。使用一些统计指标评估了所提出模型的性能。将GPR模型的输出与ANN模型和三个经验模型进行比较，并证明GPR模型在确定系数（R ²）约1，方差分别占约100％的VAF。此外，还开发了GWO以使用ANN模型选择最佳爆破参数以生成目标函数。GWO的输出显示，如果可以将孔数（nh）减少45％，将W减少8％，则PPV将减少约94％。因此，所提出的模型既适合于PPV的预测，也适合于爆炸设计参数的优化。