Blasting operations are widely and frequently used for rock excavation in Civil and Mining constructions. Flyrock is one of the most important issues induced by blasting operations in open pit mines, and therefore needs to be well predicted in order to identify the safety zone to prevent the potential injuries. For this purpose, 234 sets of blasting data were collected from Sungun Copper Mine site, and a stacked deep multi-kernel learning (SD-MKL) framework was proposed to estimate the blast induced flyrock with confidence accuracy. The proposed model uses the stacking-based representation learning framework (S-RL) to achieve deep learning on small-scale training sets. A multi-kernel learning model (MKL) is used as the base module of S-RL framework, which uses a multi-feature fusion strategy to generate multiple kernels with different kernel length in order to reduce the effort in tuning hyperparameters. In addition, this study further enhanced the predictive capability of SD-MKL by introducing the boosting method into the S-RL framework and hence proposed a boosted SD-MKL model. For comparison purpose, several existing machine learning models were implemented, i.e., kernel ridge regression (KRR), support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), ensemble deep random vector functional link (edRVFL), SD-KRR and SD-SVM. Our experimental results showed that the proposed boosted SD-MKL achieved the best overall performance, with the lowest RMSE of 0.21/1.73, MAE of 0.08/0.78, and the highest VAF of 99.98/99.24.

中文翻译：

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用于爆炸引起的飞石预测的堆叠式深度多核学习框架


爆破作业广泛且频繁地用于土木和采矿建筑中的岩石开挖。飞石是露天矿爆破作业引起的最重要问题之一，因此需要进行良好的预测，以确定安全区，防止潜在的伤害。为此，从 Sungun 铜矿现场收集了 234 组爆破数据，并提出了堆叠式深度多核学习 (SD-MKL) 框架来以置信精度估计爆炸引起的飞石。所提出的模型使用基于堆叠的表示学习框架（S-RL）来实现小规模训练集上的深度学习。多核学习模型（MKL）被用作S-RL框架的基础模块，它使用多特征融合策略生成具有不同核长度的多个核，以减少调整超参数的工作量。此外，本研究通过将boosting方法引入S-RL框架，进一步增强了SD-MKL的预测能力，从而提出了boosted SD-MKL模型。为了进行比较，实现了几种现有的机器学习模型，即核岭回归（KRR）、支持向量机（SVM）、随机森林（RF）、梯度提升决策树（GBDT）、集成深度随机向量函数链接（edRVFL） ）、SD-KRR 和 SD-SVM。我们的实验结果表明，所提出的增强型 SD-MKL 实现了最佳的整体性能，最低 RMSE 为 0.21/1.73，MAE 为 0.08/0.78，最高 VAF 为 99.98/99.24。