# Machine Learning-Based Precursor Detection Using Seismic Multi-Parameter Data
## 3 Results
### Extrapolation Inspection  
The research data used in this paper were all up to December 2021. Among the seven areas that passed the \(R\)-value evaluation test, in addition to the border regions of Qinghai and Xizang, there were also areas with high anomalies, such as the western section of southern Tianshan and the central and western sections of the Qilian Mountains. The advantageous extrapolation times for the western section of southern Tianshan and the central and western sections of the Qilian Mountains were 380 days and 170 days after the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Region**} & \multicolumn{4}{c}{**Three Elements of Prediction**} & \multirow{2}{*}{**\(R\)-Value**} \\ \cline{3-3} \cline{5-6}  & & **Time/Day** & **Intensity/\(M_{\text{L}}\)** & **Location** & \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Central Tianshan} & \multirow{2}{*}{290} & \multirow{2}{*}{\(\geq\)6.0} & Central & \multirow{2}{*}{\(R=0.55\)} \\  & & & & Tianshan & \\ \hline \multirow{2}{*}{2} & Western section of & & & Western & \\  & southern Tianshan & 380 & \(\geq\)6.3 & section of & \(R=0.44\) \\  & & & & southern & \(R_{0}=0.29\) \\  & & & & Tianshan & \\ \hline \multirow{3}{*}{3} & Border regions of & \multirow{2}{*}{260} & \multirow{2}{*}{\(\geq\)6.3} & Border area & \multirow{2}{*}{\(R=0.68\)} \\  & Qinghai and Xizang & & & and Xizang & \\ \hline \multirow{2}{*}{4} & Yutian, Xinjiang & \multirow{2}{*}{190} & \multirow{2}{*}{\(\geq\)5.5} & Yutian, & \multirow{2}{*}{\(R=0.58\)} \\  & & & Xinjiang & \(R_{0}=0.29\) \\ \hline \multirow{2}{*}{5} & Nima in Xizang & \multirow{2}{*}{100} & \multirow{2}{*}{\(\geq\)5.5} & Nyima in & \multirow{2}{*}{\(R=0.32\)} \\  & & & Xizang & \(R_{0}=0.28\) \\ \hline \multirow{6}{*}{6} & The central and & & & The central & \\  & western section of the & & & and western & \\  & Qilian Mountains & & & section of the & \\  & & & Qilian & \\  & & & & Qilian & \\  & & & & Mountains & \\ \hline \multirow{2}{*}{7} & Eastern of Southern & \multirow{2}{*}{370} & \multirow{2}{*}{\(\geq\)6.0} & Eastern of & \multirow{2}{*}{\(R=0.51\)} \\  & Xizang & & & Southern & \\ \end{tabular}
\end{table} 
Table 2: Characteristics of multi-parameter relevancy spectrum anomalies in the western Chinese mainland. multi-parameter relevancy value exceeded the threshold line and experienced abnormal peaks, respectively (Table 2). Figure 7 shows the study area of the western section of southern Tianshan. Figure 8 shows that the target earthquakes in the western section of southern Tian Shan had good earthquake-corresponding rates during the training period (January 1991 to October 2016). There were seven target earthquakes during the training period, and all high-value anomalies exceeding the threshold line appeared before the earthquakes. Taking November 2016 as the starting time for extrapolation, the relevancy curve was still at a high value. The Aktao \(M_{\rm L}7.0\) earthquake in Xinjiang occurred in the study area on 25 November 2016, which showed that the extrapolation effect was good. However, there was no high-value anomaly before the Jiashi \(M_{\rm L}6.7\) earthquake in Xinjiang on 19 January 2020, which was considered a missed earthquake. Afterward, the relevancy curve continued to rise, and it exceeded the threshold line, reaching an abnormal peak in February 2021, indicating the possibility of \(\geq\)\(M_{\rm L}6.7\) earthquakes within the study area. According to Table 2, the advantageous time for the multi-parameter relevancy spectrum in this region was 380 days. Assuming the peak time of the anomaly in February 2021 as the starting time, the validity period would be until 16 February 2022. On 13 February 2022, the Tajikistan \(M_{\rm S}6.1\) earthquake occurred near the anomaly area, corresponding to this high-value anomaly. The Aktao \(M_{\rm L}7.0\) earthquake in Xinjiang on 25 November 2016 occurred during the extrapolation test period.