Blast-Induced Ground Vibration Prediction in Coal Mining Through Regression Analysis

Abstract

In the realm of coal mining, accurate prediction of blast-induced ground vibrations is imperative to ensure structural integrity, minimize environmental impact, and maintain worker safety. This study delves into the application of various regression analysis techniques to precisely forecast ground vibrations caused by blasting activities. Leveraging methods such as linear regression, regression trees, support vector machines (SVM), and Gaussian process regression (GPR), the research aims to optimize predictive models tailored to the intricate dynamics of coal mining scenarios. Linear regression provides a foundational approach, identifying linear relationships between blast parameters and vibration outcomes. On the other hand, regression trees offer a more comprehensive perspective, capturing nonlinear patterns and interactions. Support vector machines exhibit their capacity to handle high-dimensional data, improving prediction accuracy by finding optimal hyperplanes in feature space. Gaussian process regression, a probabilistic method, not only predicts vibration levels but also provides crucial uncertainty estimates for risk assessment. By employing a combination of these regression techniques, this study seeks to unlock a comprehensive toolkit for blast-induced ground vibration prediction, taking into account various geological and operational factors. The research not only promises to improve the precision of vibration forecasting but also contributes significantly to the safety and sustainability of coal mining practises, ultimately fostering a harmonious coexistence between industrial progress and environmental preservation.