###### Abstract
However, AIC's recognition effect on a waveform with low SNR is still poor, and the local minimum value of AIC function may vary for different time windows. Additionally, the AIC method can always calculate a minimum point in any time window, regardless of whether the time window contains microseismic waveforms. The template matching method is more effective than the above two methods in detecting weak microseismic waveforms. However, the number of templates used determines its detection accuracy to a large extent, and the calculation amount also increases exponentially with the increase in the number of templates, making it difficult to process massive continuous waveform data rapidly [22]. Recently, scholars have proposed some different waveform recognition methods, such as TFA-DC [23], the fractal feature algorithm [24], and the multi-channel matching tracking algorithm [25], among others. Although these algorithms have their own advantages, they mainly aim to detect various earthquake waveforms, and their effectiveness in detecting borehole HF in coal seams is unknown. Neural network algorithms are simulations of biological neural networks, which are adaptive nonlinear dynamic network models composed of interconnected neurons [26]. In the 1940s, the first neural network model, the MP model, was developed [27]. This model was a milestone in the development of neural networks. In the 1950s, the single-layer perceptron model was proposed based on the MP model, which marked the beginning of neural networks' first rise [28]. However, it could not solve the XOR problem. In the 1980s, the Back Propagation Network (BP neural network) was introduced [29] which solved some problems that single-layer perceptrons could not. This event marked the beginning of neural networks' second rise and was introduced into the automatic recognition of seismic waveforms [30]. In the 1990s, other classification models such as SVM were applied to the field of microseismics and are still in use now [31; 32]. With the increase in neural network layers, issues such as overfitting, local optimization, and gradient diffusion began to appear, leading to the slowdown of neural network development. The 21 st century saw a significant improvement in computer functions, leading to the introduction of neural network algorithms and the concept of deep learning [33]. Among the most popular deep learning methods is the convolutional neural network (CNN), which includes classical structures like LeNet, AlexNet, ZFNet, and more. These structures have been improved by scholars [34; 35], significantly enhancing the model's feature ex traction abilities. CNN has made great strides in various fields such as image recognition, semantic understanding, and speech recognition [36; 37; 38; 39]. Similarly, microseismic waveform recognition relies mainly on data set selection and parameter optimization, where a large number of samples are used for training, and the weight matrix and bias values of neurons are continuously updated. The loss function is reduced to a minimum, the accuracy rate is increased to a maximum, and an optimal model with strong generalization performance is eventually obtained [40]. In recent years, deep learning convolutional neural networks have been widely used in microseismic detection of earthquakes, oil and gas, rock bursts, etc. [41; 42; 43]. However, there are few studies on microseismic detection of HF in coal seams. Microseismic waveforms induced by borehole HF in the coal seam in Xieqiao Coal Mine were studied in this paper. To increase data reliability, enhanced processing was carried out on the data collected by 12 sensors, including noise filtering and expert selection. This resulted in 106,743 background noise and 103,230 microseismic waveforms. A ResNet-10 model was built to recognize microseismic waveforms induced by borehole HF in the coal seam, and it was trained and tested. The experimental results showed that the trained ResNet-10 model had strong generalization ability.