Identification of Mine Water Inrush Sources Using Neural Networks

In mining operations, mine water inrush represents a frequent accident type with significant hazards and risks. Consequently, accurate identification and discrimination of water sources responsible for mine water inrush events are critically important. Neural network technology provides an effective solution for this discrimination task. As an artificial intelligence technique, neural networks simulate biological neuron systems by learning patterns from training data, enabling classification and processing of input information. For implementation, we can collect extensive datasets of historical mine water inrush cases containing key parameters such as water chemistry characteristics, flow rates, and geological indicators. The neural network architecture typically consists of an input layer receiving feature vectors, multiple hidden layers for pattern extraction, and an output layer producing classification results. Key functions would include data normalization preprocessing, backpropagation algorithm for weight optimization, and activation functions like ReLU or sigmoid for non-linear transformations. The training process involves feeding labeled water source data to the network, where supervised learning algorithms adjust connection weights to minimize classification errors. After sufficient training iterations, the model can automatically identify water sources (e.g., aquifer water, surface water, or old workings water) based on new inflow characteristics. This approach significantly enhances mine safety management capabilities and substantially reduces the probability of mining accidents through proactive water source identification.