# A Novel Method of Seismic Signal Detection Using Waveform Features
## 6 Discussion and Conclusions  
Compared with the STA/LTA, the method proposed in this paper uses the historical seismic events information in a specific area to achieve the seismic signal detection. This method has a higher detection rate and lower false alarm rate, especially for the weak seismic signals. It can advance detection performance without performing special processing such as denoising. Meantime, compared with other template matching methods, this method proposes to use event location information and the waveform feature information as prior. It uses the joint features from STA/LTA trace and time-frequency analysis of waveform instead of the raw waveform data directly, so it has better generalization performance. However, on the other hand, the location of the events in the selected region is crucial to the kernel of GP, so the new seismic signal should be happened in the same area with the historical events, ensuring the distance of the events is not too far. If the new signals come from an event in this area, the detection performance will be greatly improved.  
This paper collects earthquake events in the area around Japan within three years as the primary research object. The location information and waveform features of the seismic events are used as prior information. The Gaussian Process is applied to obtain posterior distributions for testing new signals. The test results show that the proposed method is excellent for detecting and distinguish seismic signals and noise. In this article, with the same 5% false alarm rate, the signal detection rate of traditional STA/LTA is 83.6%. However, the waveform feature model composed of the posterior distribution of each feature obtained through the Gaussian process can improve the correct detection rate to 92.5%. This method can detect some weak seismic signals, thereby improving the detection rate and reducing the false detection rate.  
The significant contribution of this paper is to use historical seismic events information to detect new seismic signals. By collecting seismic events in geographical regions where earthquakes frequently occurred as a prior can identify whether the new signal is a seismic signal or not. This algorithm can be used not only to detect weak seismic signals but also to detect aftershock event signals.  
[PERSON] made main contributions to this research, containing providing the ideas of modeling and collecting data. [PERSON] was responsible for simulation. [PERSON] and [PERSON] provided suggestions on the feasibility and innovation of the solution. [PERSON] and [PERSON] provided supervision and support for the project. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Application of data mining technology in analysis and processing of seismic waveform data.  
**Acknowledgments:** It is grateful to all colleagues who have contributed to the field of seismic research.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Class**} & \multicolumn{4}{c}{**Detection**} \\ \cline{3-6}  & & & **Signal** & **Noise** & **Sum** \\ \hline STA/LTA & Actual & Signal & 243 & 22 & 265 \\ Waveform Features & Actual & Signal & 253 & 12 & 265 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Test results of KSRS array data in 2019.