## 2 Methods
### Data Collection and Training  
Xieqiao Coal Mine is a significant mining site in the Huainan mining area, classified as a coal and gas outburst mine with high gas content and pressure. As per the government's 'Rules for the Prevention and Control of Coal and Gas Outburst', coal mining operations may only begin once the coal seam gas extraction reaches standard levels. To enhance gas extraction efficiency, Xieqiao Mine adopted borehole HF technology, which involves boring through the floor roadway for coal seam pressure relief and permeability improvement. Moreover, a high-precision microseismic monitoring system was installed in the Xieqiao Coal Mine to track the HF in the coal seam. The microseismic system comprises a high-sensitivity wide-band speed microseismic sensor with a 200 v/m/s sensitivity, a frequency band of 4.5 Hz to 1500 Hz, and a system sampling rate of 4 kHz. The sensor is mounted by boreholes to shield background noise. Lastly, to ensure the deep learning ResNet-10 model is effectively trained, a dataset with enough samples covering microseismic waveforms and background noise with various complex characteristics is required. We monitored the waveform and intercepted a total of 5773 microseismic waveforms, which we marked as 1. Additionally, we recorded 8211 segments of background noise that we randomly selected and marked as 0. To avoid any errors caused by human selection, we double-checked and corrected the selected data. The magnitude of the microseismic events resulting from borehole HF in coal seams is primarily in the range of \(-3\) to 0 (2 to 5.4\({}^{10}\) J), and the duration of microseismic waveforms can reach up to 1.2 s. To include microseismic waveforms of varying magnitudes in waveform segments, we set the length and step size of ResNet-10 sliding window to 1.5 s. To make the dataset more suitable for the deep learning ResNet-10 model, we augmented the dataset with noise and filters using data expansion technology. We added noise to the microseismic waveforms with 12 different gains and applied 5 different degrees of filtering to make the expanded microseismic waveforms and the background noises roughly equal. The low-pass filter can have five different cut-off frequencies: 400 Hz, 500 Hz, 600 Hz, 700 Hz, and 800 Hz. Adding noise to a waveform is a five-step process that is described as follows:  
1. In order to generate a sequence of numbers with standard normal distribution, we use the randn function in Matlab (2021 version). The length of this sequence is the same as the length of the waveform segment. 2. The series generated by the randn function may not necessarily have a mean value and variance of 0 and 1, respectively. Therefore, we subtract the mean value of the series to reduce the impact of these deviations on subsequent data processing. 3. We calculate the db gain of the output signal and the input signal using Formula (15). Next, we multiply this gain by the sum of squares of the waveform segments, and then multiply it by the length of the waveform segments. Finally, we take the reciprocal square root of the result. We perform these operations to ensure that the amount of noise added to each waveform in the segment remains within a reasonable range. 4. In order to eliminate the influence of differences between data in the series generated in Step (2), we calculate the standard deviation of the series. We then multiply this standard deviation by each number in the series successively to obtain a new series. 5. We divide the result of Step (4) by the result of Step (3) to obtain the noised sequence. Finally, we add this sequence to the waveform fragment to obtain the waveform fragment after noise addition. \[db=10*log_{10}{}^{(P_{8}/Pn)}. \tag{13}\]  
In Equation (13), \(P_{s}\) represents the output signal's voltage amplitude, \(P_{n}\) represents the input signal's voltage amplitude, and \(db\) represents the gain. Figure 4 shows a denoising and filtering process for an effective waveform, and Figure 5 shows a denoising process for background noise.