# Machine Learning-Based Precursor Detection Using Seismic Multi-Parameter Data
## 1 Introduction  
Various seismological parameters may exhibit different degrees of precursor anomaly characteristics before major earthquakes and reveal certain laws of earthquake development from different perspectives. There were many studies on the \(b\) value within seismological parameters [1; 2; 3]. The \(b\) value is a critical parameter in seismic hazard studies [4], and a high \(b\) value indicates a larger proportion of small earthquakes, and vice versa. In addition, earthquake frequency is also a commonly used seismological parameter internationally [5; 6]. What would be the effect of integrating these seismic parameters for analysis? There has been relatively little comprehensive study of seismic parameters, which could solve the limitations of single parameter and method approaches [7]. The comprehensive analysis of multiple methods based on the quantitative description of a single anomaly is of great significance in earthquake research. Based on this idea, the study was conducted on the quantitative identification and extraction of anomalies from observation data using mathematical-statistical methods. For example, [PERSON] [8] transformed the time series of original data into a probability time series based on the corresponding relevancy spectrum in different investigation durations, and used the multi-point cumulative moving average method to obtain the time curve of average probability; then, the earthquakeprecursor anomalies were identified, providing quantitative single factor data for the comprehensive method. [PERSON] [9] used the multi-point group slope method and composite information flow method to convert the deformation data into the'standardized information curve'. [PERSON] [10] quantitatively identified and extracted anomalies from various seismic parameter data in the western section of the southern Tianshan of Xinjiang by using the multi-parameter sliding extreme-value relevancy based on the earthquake-corresponding relevancy spectrum. [PERSON] also conducted a long-term tracking study on the central and southern sections of the Tanlu fault, utilizing the earthquake-corresponding relevancy spectrum of seismic multi-parameters, and found that the earthquake-corresponding efficiency calculated by combining multiple advantageous parameters was indeed better than that of a single parameter [11]. Some studies also found that although some calculated factors and models were different, the regional scales of the predicted earthquake were consistent [12; 13; 14; 15]. Perhaps more earthquake factors could more easily predict the spatial-temporal and intensity information of earthquakes. Therefore, it is possible to try combining multiple different factors within a unified and reasonable physical framework for the extraction of seismic anomalies. For example, establishing a physically reasonable framework that combines the pattern informatics (PI) prediction method [16; 17] with the load/unload response ratio (LURR), state vector (SV), and accelerating moment release (AMR) method [18] could be considered. Alternatively, the LubRR method could be combined with other methods for seismic hazard assessment [19]. Scholars have also attempted to use the LubRR method to study the electromechanical coupling process before large earthquakes, using geoelectric data and Benioff strain data from small earthquakes as input [20], and the results were both well. These studies fully demonstrated the advantages of using multiple different factors and models for comprehensive calculation. Recently, there have been many studies on the application of machine learning for earthquake precursor mining and analysis. Possible precursors from the surface to the ionosphere using machine learning techniques were analyzed [21]. And earthquake precursors could be detected by a novel machine learning-based technique with GPS data [22]. The strongest earthquakes in China occur in the western part of the Chinese mainland.