## 1 Introduction  
As a currently more advanced and effective ground-pressure-monitoring technology, microseismic monitoring is increasingly used in the mining field [1, 2, 3]. Microseismic monitoring mainly uses waveform analysis of vibration signals to obtain time, space and intensity information of microseismic events [4]. However, the signals collected by the sensor are mixed with interference signals such as blasting and mechanical noise. The blasting-vibration signal is the most difficult to identify because its time-frequency characteristics are closest to the coal-and-rock-mass-fracture signal. At present, this problem mainly depends on manual identification. Affected by individual factors, there may be misjudgment, which greatly affects the accuracy of mine-microsise-information processing [5, 6, 7]. Therefore, how to extract effective coal-and-rock-mass-rupture signals from many waveform signals for mine-dynamic-disaster prediction is particularly important [8]. Time-frequency analysis is one of the main methods to identify microseismic-signal waveforms in micro-mines. For the effective identification of coal-and-rock-mass-rupture signals and blasting-vibration signals, scholars at home and abroad have carried out extensive research on time-frequency analysis [9]. [PERSON] et al [10] extracted the characteristics of microseismic signals by the EEMD-SVD method and identified the types of microseismic signals by the ELM algorithm, and obtained a high accuracy rate. [PERSON] et al. [11] adopted a mine-microseismic-event-identification method combining improved wavelet decomposition and an extreme learning machine (ELM). [PERSON] et al. [12, 13] used EMD to recombine the microseismic signal to find its fractal dimension, and constructed the Fisher method with the slope value of the peak starting-up trend line as the feature vector for signal recognition. [PERSON] and [PERSON] [14] established a high-precision microseismic-event-identification model based on source parameters through logistic regression and a neural network. [PERSON] et al. [15] studied the performance of ten commonly used machine-learning models for microseismic/blasting-event recognition. Although the above methods are widely used in the field of microseismic monitoring and have achieved significant results, they also have some shortcomings. The Fourier transform is generally not suitable for processing microseismic signals with non-stationary characteristics. Traditional EMD lacks a strong theoretical basis and is prone to modal aliasing and the end effect in the process of signal decomposition. There is a misjudgment phenomenon in the process of selecting effective IMF components based on experience. Wavelet analysis and wavelet packet decomposition have poor adaptive decomposition performance [16]. The Local Mean Decomposition (LMD) method mainly implements signal-adaptive time-frequency analysis through an iterative process [17], and has many applications in fault detection, non-stationary-signal analysis [18], and seismic-signal recognition [19]. [PERSON] et al. [20] proposed a novel Adaptive Complementary Ensemble Local Mean Decomposition (ACELMD) for complex underwater acoustic signals. Through simulation tests, they found that this method reduces modal aliasing. [PERSON] et al. [21] used a compound interpolation envelope (CIE) LMD method to evaluate the local non-stationary characteristics of mechanical vibration signals. The relevant literature shows that the LMD method overcomes the problem of EMD mode aliasing, and has great advantages in suppressing the end effect and processing non-stationary signals [22]. The probabilistic neural network (PNN) is a feedforward neural network implemented by Bayesian strategy, which is suitable for pattern-recognition applications. For example, [PERSON] et al. [23] used a probabilistic neural network (PNN) to improve the accuracy of seabed-sediment identification. [PERSON] et al.