# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
###### Abstract
[23] proposed an automatic classification method for seismic records in areas with few seismographs which makes use of support vector machines (SVM). According to this method, the seismic records are divided into four phase windows after they were filtered by 20 filters. Subsequently, the short-term averages of all filter channels and phase windows are computed. The dataset consists of 80 parameters that play the role of training the SVM model will be assigned to each event station pair. [PERSON] and [PERSON] [24] adopted logistic regression and neural network classification techniques to classify seismic records based on 14 parameters obtained from the ESG microseismic monitoring system and achieved a relatively high classification accuracy. [PERSON] et al. [25] presented a new method for the detection and classification of seismic events at an array station which combined the progressive multi-channel correlation detector and a statistical time series classifier based on hidden Markov models. [PERSON] [26] used a multivariate maximum-likelihood Gaussian classifier technique to discriminate microseismic events and mine blasts. It utilizes four seismic event characteristics and quantifies a probability that a particular event belongs to a population of blasts. [PERSON] et al. [27] chose spectral characteristics, the event time in hours and two physical characteristics of waveforms as discrimination features, and then developed a model based on statistics through the use of Fisher discriminant analysis which achieved a high discrimination accuracy. [PERSON] et al. [28] selected nine ratios of the energies contained within predetermined velocity windows and average distance as feature parameters and then uses nonlinear methodologies including naive Bayesian classifier to discriminate seismic events and nuclear explosions. [PERSON] et al. [13] adopted an artificial neural network for the classification of microseismic events and mine blasts. Twenty-two seismic parameters from the microseismic monitoring system were selected as characteristic parameters, and the effect of each parameter on classification was analyzed by principal component analysis. However, the discrimination accuracy of some of the above intelligent algorithms is not very high, and there is still potential for improvement. In addition, no algorithm is suitable for all problems and all field sites are based on the well-known 'no free lunch' theorem, and the combination of the extreme learning machine and the particle swarm optimization algorithm has not been used to discriminate seismic events and blasts. In this paper, an artificial intelligence model called PSO-ELM based on the extreme learning machine optimized by the particle swarm optimization algorithm is applied to discriminate microseismic events and blasts in underground metal mines. In order to check the discrimination performance of PSO-ELM, experiments were also carried out with the original extreme learning machine model and other commonly used intelligent discrimination models, including the backpropagation neural network, the naive Bayesian classifier and Fisher discriminant analysis. All the algorithms were implemented in MATLAB R2016b. The discrimination performance of the five intelligent discrimination models above is compared and analyzed. The research results obtained can provide a new method for the discrimination of microseismic events and blasts, and this is helpful to promote the safe and smooth operation of mines.