# Machine Learning-Based Precursor Detection Using Seismic Multi-Parameter Data
## 3 Results
### Risk Assessment of the Multi-Parameter Relevancy Spectrum in the Western Chinese Mainland  
The \(R\)-value evaluation is a widely recognized method used for evaluating the effectiveness of earthquake prediction [24; 25]. The \(R\)-value evaluation is calculated by the difference between the earthquake prediction accuracy rate and the time occupancy rate of prediction. The specific formula is as follows:  
\[R=A-O=\frac{N_{a}}{N_{t}}-\frac{S_{a}}{S_{t}} \tag{8}\]  
\(A\) represents the earthquake prediction accuracy rate, \(O\) represents the time occupancy rate of the predicted area, \(N_{a}\) represents the number of predicted earthquakes successfully, \(N_{t}\) represents the total number of earthquakes that should be predicted, \(S_{a}\) represents the time occupied by prediction, and \(S_{t}\) represents the total time spent on prediction research.  
Assuming that earthquakes occur independently of each other, the number of earthquakes that occur within a certain period should follow the discretization state of the Poisson distribution. The probability of a single earthquake occurring during this period is represented by the time occupancy rate, \(O\), of the predicted area, and the corresponding significance level should be satisfied as follows [26]:  
\[\begin{array}{l}\alpha=\sum\limits_{i=N_{a}}^{N_{t}}\left[\left(N_{ti}O^{i} (1-O)^{N_{t}-i}\right)\right]\\ N_{ti}=\frac{N_{t}!}{B[(N_{t}-i)!]}\end{array} \tag{9}\]  
Figure 6: The spatial evolution of the MSER of the border regions of Qinghai and Xizang from January 2008 to September 2010.  
In the formula, \(\alpha\) represents the significance level, taken as 5%. Based on the known \(A\) value, the value \(O\) is calculated, and the corresponding \(R\) score for the significance level \(\alpha\) is obtained and represented by \(R\). When \(R>R_{0}\), the predicted result is considered to have high statistical significance [27; 28].  
In this paper, the \(R\)-values of the multi-parameter calculation results of 12 tectonic regions in the western Chinese mainland were evaluated, and finally, 7 tectonic regions passed the test (Table 2), namely, central Tianshan, the western section of southern Tianshan, the border regions of Qinghai and Xizang, Yutian in Xinjiang, Nima in Xizang, the central and western sections of the Qilian Mountains, and the eastern section of southern Xizang. The criterion for determining anomalies was that the relevancy value exceeded the threshold line, and an abnormal peak appeared. The time among the three elements was considered the advantageous extrapolation time when calculating the optimal \(R\)-value by different times (Table 2). The extrapolation time period started with the abnormal peak time. Taking central Tianshan in Table 2 as an example, if its relevancy value exceeded the threshold line and reached an abnormal peak in October 2019, its advantageous extrapolation time was 290 days. Therefore, the validity period should be until July 2020. The intensity of the predicted earthquake was based on the minimum magnitude of target earthquakes used for simulation training in the training period. The predicted location refers to the area studied and calculated.