# A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing
## 3 Results
### Application in Automated Mining-Induced Microseismic Events Classification  
Mining-induced microseismic events' monitoring is crucial for predicting rock mass hazards [46,47]. Mining-induced microseismic events tend to be small in magnitude and require sensitive sensors to acquire. However, in addition to collecting microseismic signals, sensitive sensors will also collect all kinds of interference signals, such as blasting signals, mechanical vibration signals, etc. Picking out accurate microseismic signals is necessary to process mining-induced microseismic events further. At the same time, the mining-induced microseismic data collected in a day are enormous, so the monitoring effect must be improved by using intelligent processing methods [48]. Based on this engineering background, we designed an experiment to verify the reliability of our wavelet selection method. This method was employed to train a convolutional neural network fora dataset that includes 5400 mining-induced microseismic signals, 5400 blasting signals, and 5400 mechanical vibration signals. According to the method proposed in this paper, we combined these data into one large dataset, denoted as M\({}_{1}\). Based on the commonly used wavelets listed in Tables 1 and 2, the decomposition stability \(w\) of each wavelet concerning M\({}_{1}\) was calculated separately, and the calculation results are recorded in Table 2. As shown in Table 2, for dataset M\({}_{1}\), the wavelet is btio3.1, which maximizes the decomposition stability \(w\). As shown in Figure 6, to verify the suitability of using our method, we used the selected wavelet rbio3.1 for the automatic classification of microseismic signals. Moreover, we also randomly selected five wavelets for comparison experiments, including haar, db2, sym2, coif3, and bior3.1. Then, the original microseismic signal contains 2000 sampling points, which are decomposed into three levels of wavelet packet decomposition to obtain eight wavelet packet coefficients of the signal, each containing 250 sampling points (infact, there may be more than 250 sampling points, but these are uniformly cropped to just 250 sampling points for the convenience of calculation). Eight wavelet packet coefficients composed of a matrix are used as the feature matrix for deep learning. The deep learning model used in this paper contains two convolutional layers with a 3 \(\times\) 3 convolutional kernel, a max-pooling layer, and two fully connected layers. It is necessary to state that we used a simple deep learning model here, whose purpose is to highlight the effectiveness of wavelet selection methods. Therefore, a simple deep learning model can downplay the impact of the model on the experimental results. After all, it is not the accuracy of the classification problem that is pursued in this paper. We divided the dataset M\({}_{1}\) into a training set and a test set. The training set was used to train the deep learning model in Figure 6 to classify signals. The training set contains 5000 mining-induced microseismic signals, 5000 blasting signals, and 5000 mechanical vibration signals, and the test set contains 400 mining-induced microseismic signals, 400 blasting signals, and 400 mechanical vibration signals. We conducted three sets of comparison experiments for different wavelet-generated datasets, and for each dataset, the deep learning model was trained with 50 epochs. After the model was trained, it was tested using the test set, and the experimental results are shown in Table 3. The classification accuracies of the wavelet rbio3.1 are 93.55%, 92.75%, and 93.08%, with an