# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
## 2 Data
### Discriminant Parameters and Sample Dataset  
The microseismic event can be regarded as the sudden release of potential or stored energy in the rock, which is then radiated in the form of seismic waves [29; 30]. Development or production blasting in mines is a rapid chemical reaction of an explosive substance which causes crushing and fracturing in the surrounding rock mass or the cemented paste  
Figure 2: The IMS system in Fankou Lead-Zinc Mine. backfill [31; 32]. It initiates a disturbance that propagates outwards, predominantly in the form of a compressional seismic wave [26]. Blasts or explosions usually radiate waves of higher frequency than microseismic events [33], and various combinations of seismic parameters, such as the ratio between seismic moment and corner frequency, apparent stress, etc., can be used to obtain the difference in frequency components between blasts and microseismic events [26]. The seismic wave radiation pattern of microseismic events is also different from that of blasts. A blast radiates predominantly P-waves from the source, while the microseismic event caused by shear cracking or slip on a contact face radiates P-waves that are weaker than S-waves [26; 30]. The amplitude relationship between P-waves and S-waves can be obtained by comparing \(M_{p}\) with \(M_{s}\), which are the seismic moments estimated based on P-waves and S-waves, respectively. This means that the ratio of \(M_{s}\) to \(M_{p}\) and its equation, such as the ratio of \(P_{s}\) to \(P_{p}\), can be used as the characteristic parameters of the seismic wave radiation pattern, where \(P_{s}\) and \(P_{p}\) are the total potency of S-waves and P-waves, respectively. In addition, generally, mines conduct blasting operations within a relatively fixed period of time. Therefore, an event that occurred during this period of time is more likely to be a blast than a microseismic event [26]. Based on the above differences between blasts and microseismic events, and combined with previous research results [13; 24], 22 seismic parameters, including the longitudinal direction coordinates of the event location (\(E\)), the latitudinal direction coordinates of the event location (\(N\)), the depth direction coordinates of the event location (\(D\)), the associated vectorial error sum (\(\Delta r\)), the origin time of seismic records (\(t\)), apparent stress (\(\sigma_{a}\)), apparent volume (\(AV\)), corner frequency (\(f_{c}\)), dynamic stress drop (\(\Delta\sigma_{d}\)), total radiated energy (\(E_{0}\)), S-wave radiated energy (\(E_{s}\)), P-wave radiated energy (\(E_{p}\)), S-wave to P-wave energy ratio (\(E_{s}/E_{p}\)), the number of sensors used in the location of the event (\(N_{s}\)), the source radius (\(r_{0}\)), static stress drop (\(\Delta\sigma\)), seismic moment (\(M_{0}\)), moment magnitude (\(M_{up}\)), the total potency of the P- and S-wave (\(P\)), the total potency of the S-wave (\(P_{s}\)), the total potency of the P-wave (\(P_{p}\)) and the S-wave to P-wave potency ratio (\(P_{s}/P_{p}\)), were selected as the preliminary characteristic parameters for discriminating microseismic events and blasts in this work. One thousand six hundred events, 50% of which were microseismic events, while the remaining 50% were blasts, were randomly chosen from the IMS database as the sample dataset to ensure the applicability of the PSO-ELM method to different microseismic signal data. These 1600 events were recorded from 2016 to 2020, and the number of events in the sample dataset is considerable, which is helpful in avoiding the overfitting phenomenon.