# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
###### Abstract  
To reduce the workload and misjudgment of manually discriminating microseismic events and blasts in mines, an artificial intelligence model called PSO-ELM, based on the extreme learning machine (ELM) optimized by the particle swarm optimization (PSO) algorithm, was applied in this study. Firstly, based on the difference between microseismic events and mine blasts and previous research results, 22 seismic parameters were selected as the discrimination feature parameters and their correlation was analyzed. Secondly, 1600 events were randomly selected from the database of the microseismic monitoring system in Fankou Lead-Zinc Mine to form a sample dataset. Then, the optimal discrimination model was established by investigating the model parameters. Finally, the performance of the model was tested using the sample dataset, and it was compared with the performance of the original ELM model and other commonly used intelligent discrimination models. The results indicate that the discrimination performance of PSO-ELM is the best. The values of the six evaluation indicators are close to the optimal value, which shows that PSO-ELM has great potential for discriminating microseismic events and blasts. The research results obtained can provide a new method for discriminating microseismic events and blasts, and it is of great significance to ensure the safe and smooth operation of mines. microseismic event; mine blast; artificial intelligence; particle swarm optimization; extreme learning machine +
Footnote †: journal: Medicaldue to it depending on an operator's experience, and delays in discriminating results [12]. In addition to the blast signal, other interference signals mentioned above have a significant difference in waveform from the microseismic signal, and so it is relatively easy to distinguish them from microseismic signals. However, for the blast signal, its waveform is very similar to the microseismic signal, which greatly increases the probability of misjudgment in manual discrimination. Therefore, an efficient and reliable method is urgently needed for discriminating the huge number of microseismic records in mines. Researchers worldwide have conducted many studies to differentiate seismic events from human-made explosions, including nuclear explosions and mining blasts [12; 13], and many different techniques have been proposed. For example, [PERSON] [14] discriminated earthquakes and underground explosions in the Western United States by means of the Rayleigh-wave spectral ratio of short- to long-period energy, which belongs to the waveform spectrum analysis method. [PERSON] et al. [15] used magnitude and amplitude ratios to discriminate small earthquakes and blasts at local distances, and obtained a good discrimination performance. [PERSON] et al. [16] used the method based on the spectral amplitude ratio of the vertical components of P-waves and S-waves in a specified frequency range to discriminate earthquakes and blasts. [PERSON] et al. [17] utilized aftershock characteristics to differentiate explosions from earthquakes. Among them, artificial intelligence techniques are widely reported and effective, and have been used in many other fields, such as the prediction of blast-induced rock movement [18], the optimization of postblast ore boundary determination [19], the prediction of rockbursts [20], the prediction of blast-induced air overpressure [21], and the prediction of the strength of the fiber-reinforced cemented paste backfill [22]. For signal discrimination, support vector machines [23], neural networks [13], logistic regression [24], hidden Markov models [25], the Gaussian classifier [26], the Fisher classifier [27] and the naive Bayesian classifier [28] have been widely applied. Usually, some characteristic parameters obtained from the events are selected to represent the original signal and then are used as the input parameters of the models established by intelligent algorithms to discriminate events. For instance, [PERSON] et al.