# Machine Learning-Based Precursor Detection Using Seismic Multi-Parameter Data
## 4 Discussion  
The abnormal patterns of different parameters before the target earthquakes were different. There were differences in the starting times, peak values, and end times of the anomalies. The comprehensive analysis method of seismic parameters can combine single parameters related to physical processes at different stages of earthquake preparation, which could extract the comprehensive abnormal characteristics during the earthquake preparation process more accurately. [PERSON] (2020) conducted a comprehensive multi-parameter study on earthquakes from 1979 to 2008 in the western section of southern Tianshan, and the results showed that target earthquakes in the study area exhibited significant high-probability anomalies of multiple parameters 1-2 years prior to the earthquakes. This research conclusion was consistent with the advantageous extrapolation time of 380 days in the western section of southern Tianshan in this paper. In addition, the anomaly-corresponding rate is the ratio of the number of anomalies corresponding to the target earthquake to the total number of anomalies. The earthquake-corresponding rate is the ratio of the number of target earthquakes with anomalies before the earthquake to  
Figure 11: The MSER changes of the central and western sections of the Qilian Mountains. Figure 12: The spatial evolution of the MSER of central and western sections of the Qilian Mountains from June 2020 to September 2021.  
the number of target earthquakes. By statistically analyzing the anomaly-corresponding rates and earthquake-corresponding rates of single parameters and multiple parameters in different inspection periods and regions of Xinjiang (Table 3), it was found that the anomaly-corresponding rates and earthquake-corresponding rates of multiple parameters were indeed higher than the predictive efficiency of a single parameter. Related studies had also found that--when combined with methods such as PI, LURR, SV, and AMR--the earthquake prediction performance was better than that of a single method [18]. At present, this study mainly applied different seismological parameters for comprehensive analysis through machine learning. This method could also comprehensively calculate other observation data of earthquake precursors, such as deformation, electromagnetic, and underground fluid anomalies. It was even possible to merge and summarize all seismic observation data to complete a comprehensive probability analysis. At the same time, by analyzing the spatial evolution of the MSER during the extrapolation test periods, it was found that the target earthquakes generally occurred after the maximum amplitude of anomaly enhancement was reached, and the epicenter was located in or near the anomaly concentration area. This indicated that the model obtained by using this method for machine learning of historical earthquakes had a good extrapolation effect, which can provide a reference for the seismic hazard of the study area. In addition, the \(R\)-value in this manuscript is different from the linear correlation coefficient, \(R\), which measures the relationship between two sets of data. The \(R\)-value evaluation is calculated by the difference between the earthquake prediction accuracy rate and the time occupancy rate of prediction, as outlined in Formula (8). The best prediction performance corresponds to paying the minimum cost (O\(\rightarrow\)0) at the highest accuracy rate (\(A\)\(\rightarrow\)1), that is, \(R\)\(\rightarrow\)1. On the contrary, the worst prediction performance corresponds to paying the maximum cost (O\(\rightarrow\)1) at the lowest accuracy rate (\(A\)\(\rightarrow\)0), that is, \(R\)\(\rightarrow\)\(-\)1. It is generally believed that when \(R\)\(>\) 0, the success rate of the forecasting is higher than the random probability, and this result has a certain predictive effectiveness [25]. \(R_{0}\) represents the derived \(R\)-value based on the significance level of 5% (95% confidence level).