## 4 Denoising and Prediction of Optical Fiber Monitoring Signals Based on EEMD-DE-SVD and DBO-LSTM-BP
### Prediction Model  
This paper combines LSTM and BP neural network algorithms. The computer is configured with Windows 10, using PyCharm 2021.2.2 and the TensorFlow 2.2 deep learning framework, with training and testing sets divided in an 8:2 ratio. The model employs a three-layer LSTM network structure and a two-layer BP neural network structure. Due to  
Figure 8: Dispersion entropy of IMF components for borehole 1, measurement point 8.  
Figure 9: Dispersion entropy ratio of IMF components for borehole 1, measurement point 8.  
the difficulty of manual parameter optimization under these conditions, the dung beetle optimization (DBO) algorithm is introduced to optimize the parameters of the combined neural network structure.  
The goal of the DBO algorithm is to find the x that minimizes (or maximizes) the objective function f(x). This algorithm is known for its strong search capabilities and adaptability, making it widely used in machine learning, deep learning, and other fields.  
LSTM (long short-term memory) is a special type of recurrent neural network (RNN) with a structure designed to learn long-term dependencies, as shown in Figure 10. The LSTM introduces three special \"gates\" that provide its long-term memory capability. The input gate updates the cell state based on the current input and the previous hidden state, while the forget gate selectively forgets information to prevent minor details from affecting the predictions. The output gate outputs a hidden state that includes information from the previous input, which, along with the current input, is passed to the input gate of the next unit, facilitating the information transfer and enabling the model to maintain memory.  
The BP neural network consists of an input layer, a hidden layer, and an output layer. The input layer is the entry point for information, the hidden layer is where information is processed, and the output layer provides the final result. The process of the BP neural network is mainly divided into two stages: the first stage is the forward propagation of the signal, from the input layer through the hidden layer to the output layer; the second stage is the backward propagation of the error, from the output layer to the hidden layer and finally to the input layer, sequentially adjusting the weights and biases from the hidden layer to the output layer and from the input layer to the hidden layer. Figure 11 is a schematic diagram of the neural network layers.  
Figure 11: Neural network hierarchy diagram.  
Figure 10: LSTM neural network graph.  
The BP neural network model has high requirements for the quality of data preprocessing and requires a large number of iterative calculations during the training process, which can easily lead to local optima and difficulty in converging to the global optimum. Section 2.1 describes that the EEMD-DE-SVD algorithm can decompose and denoise the signal, improving the quality of data preprocessing. The DBO algorithm can find the global optimum in the search space. However, when applying genetic algorithms to specific problems, there is no standard hyperparameter setting rule, so hyperparameter optimization is needed during model training iterations to find suitable hyperparameters. Additionally, the BP neural network model faces the problem of gradient disappearance due to gradient propagation, which causes slow convergence between the actual values and error values. Therefore, this paper introduces the LSTM (long short-term memory) network to avoid the gradient disappearance problem.  
Figure 12 shows the combined neural network prediction model incorporating the above four algorithms. To verify the prediction performance of this model, the actual deformation optical fiber monitoring data from the 5-1# coal pillar section of the Daliuta Huojitu Well is used for model training and testing.