Unearthing the potential of deep learning in predicting rock explosions
Deep learning, a subset of artificial intelligence, is no longer limited to the realms of technology, medical diagnostics, and finance. That potential is being discovered in the unexpected field of underground mining. Specifically, deep learning is being used to predict rock explosions, a major safety concern in the mining industry.
Rock burst refers to the violent eruption of rock that can occur in deep mines due to high stress levels in the rock. The possibility of predicting these events is of great importance for the safety of coal mine operations and the protection of both personnel and property underground. However, the complexity and unpredictability of these events poses significant challenges to current prediction methods.
Introducing a new approach: BiLSTM using differential evolution algorithm
A state-of-the-art approach based on deep learning is proposed to overcome these challenges. A bidirectional short-term and short-term memory network (BiLSTM) with a differential evolution algorithm and an attention mechanism for training is introduced. This combination results in a predictive model that can accurately determine the critical period of rock failure.
BiLSTM is a type of recurrent neural network that can capture patterns over time, making it ideal for predicting transient events such as rock explosions. Differential evolution algorithms help optimize network performance, while attention mechanisms allow the model to focus on important information, improving prediction accuracy.
Outperforming existing models: The strength of deep learning
A comparative analysis of different predictive models further demonstrates the superiority of deep learning. For example, a method for predicting the peak shear strength of rock joints based on machine learning was developed and compared with existing models. The results showed that deep learning performed better than existing models, enhancing its potential to more reliably predict rock-related events.
Understanding the impact of mining on land subsidence
The potential of deep learning is being exploited to predict rock explosions, but the impact of mining on land subsidence is also an area of ​​concern. Accurate estimation of subsidence rates is important for land protection and reclamation. One study suggests using the average General Strength Index (GSI) value of the surface soil as an indicator to estimate the subsidence rate. A calibrated numerical model was used to establish the relationship between his GSI value of overburden and subsidence rate.
Conclusion: Deep learning – a promising tool for mine safety
As the mining industry continues to drill deeper into the Earth’s crust, the risk of rock explosions increases and the need for advanced predictive techniques increases. Deep learning has the ability to process vast amounts of data and identify patterns, making it a promising tool for increasing mining safety. By harnessing that potential, the industry can predict and prevent rock explosions, thereby saving the lives of underground workers and ensuring the continuity of mining operations.