# Machine Learning-Based Precursor Detection Using Seismic Multi-Parameter Data
## 3 Results  
### Multi-Parameter Relevancy Spectrum Calculation  
The most reasonable single parameter and its threshold settings, within the probability spectrum of earthquake occurrence and the value range spectrum in each study area, were analyzed through time scanning with different statistical window lengths and step sizes. After years of analysis and experience summaries, the optimal seven parameters were ultimately selected to participate in the calculation of each research area, namely, the \(b\) value, \(M\) value (missing earthquake), and \(\eta\) Value--these three parameters could describe the relationship between seismic magnitude and frequency; the \(D\) value describes seismic hazard; the \(M\) value denotes the intensity factor; the \(N\) value denotes the earthquake frequency, which describes the intensity changes of seismic activity; and the \(Rm\) value is the environmental modulation parameter. A large portion of the computational analysis was conducted across different research areas. Using the three study areas of border region as examples, e.g., Qinghai and Xizang, the western section of southern Tianshan, and the central and western sections of the Oliian Mountains, different time scanning scales were applied for calculation: 3 months, 6 months, and 12 months. The results showed that the curve did not change much, but the anomaly and earthquake-corresponding rates of the three study areas were relatively high at the 12-month scale (Table 1). Therefore, this study uniformly used a 12-month time scanning scale for all study areas, and the seismic parameters and thresholds used in all study areas were also the same, which could help to avoid errors caused by differences in the time scale and parameters. Taking the abnormal changes in the border areas of Qinghai and Xizang, where the Maduo \(M\)7.4 earthquake occurred on 22 May 2021, as an example, and based on the previous summary and analysis that 12 months was a good sliding window length for calculation, the window lengths of the time calculations for seven parameters were all selected as 12 months, and the calculation step sizes were all 1 month. The 6-point sliding average and 3-point sliding extreme-value algorithms were used to calculate the multi-parameter sliding extreme-value relevancy of earthquake data from January 1991 to December 2021. According to the time distribution of target earthquakes in the study area, it is necessary to ensure that the training period had sufficient sample data and a certain number of target earthquakes to test the extrapolation effect. Therefore, the deadline for the training period here was selected as December 2020, and the target earthquakes in the study area were set as \(M_{\rm L}\geq 6.3\) earthquakes. The results that began in January 2021 were extrapolation values after the machine learning, and the threshold of the probability curve was taken as 0.5 times the standard deviation. The threshold line was obtained through a large number of calculations in 12 study areas in the western Chinese mainland. It was found that when 0.5 times the standard deviation of the multi-parameter probability result was taken as the threshold line, the corresponding effect between the probability value anomaly and the target earthquake was the best. Therefore, in order to eliminate the error caused by using different threshold lines, all study areas uniformly used 0.5 times the standard deviation as the threshold line. Figure 3 shows the study area of the border regions of Qinghai and Xizang. Figure 4 shows the MSER results of the study area. The results show that the earthquake-corresponding rate was good during the training period from January 1991 to December 2020. During the training period, a total of seven target earthquakes occurred, all with high-value anomalies exceeding the threshold line before the earthquakes. The extrapolation period began in January 2021. The probability curve had been in an abnormally high-value state and reached its peak in January 2021.