# A Novel Method of Seismic Signal Detection Using Waveform Features
## 5 Results
We use the recall and precision indicators to further evaluate the performance of algorithms, as shown in Table 6. From the table, the recall and precision of our algorithm are 0.925 and 0.931, respectively. These indicators exceed the other two algorithms, and especially the recall is improved by 8.9% and 5.4% compared to the other two methods respectively. The F score of our algorithm is also the biggest one, which means the performance of our detector is better. In summary, we conclude that the multi-features joint probability model method can improve the detection performance compared to the STA/LTA. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Accuracy** & **Precision** & **Recall** & **F Score** \\ \hline STA/LTA & 0.902 & 0.924 & 0.836 & 0.878 \\ C\_STA/LTA & 0.916 & 0.927 & 0.871 & 0.898 \\ Waveform Features & 0.939 & 0.931 & 0.925 & 0.928 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Efficiency of various algorithms. \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 \multirow{3}{*}{STA/LTA} & \multirow{3}{*}{Actual} & Signal & 123 & 24 & 147 \\  & & Noise & 10 & 190 & 200 \\  & & Sum & 133 & 214 & 347 \\ \hline \multirow{3}{*}{C\_STA/LTA} & \multirow{3}{*}{Actual} & Signal & 128 & 19 & 147 \\  & & Noise & 10 & 190 & 200 \\  & & Sum & 138 & 209 & 347 \\ \hline \multirow{3}{*}{\(\alpha\)} & \multirow{3}{*}{Actual} & Signal & 123 & 24 & 147 \\  & & Noise & 10 & 190 & 200 \\  & & Sum & 133 & 214 & 347 \\ \hline \multirow{3}{*}{\(\rho\)} & \multirow{3}{*}{Actual} & Signal & 131 & 16 & 147 \\  & & Noise & 10 & 190 & 200 \\  & & Sum & 141 & 206 & 347 \\ \hline \multirow{3}{*}{log(\(\gamma\))} & \multirow{3}{*}{Actual} & Signal & 127 & 20 & 147 \\  & & Noise & 10 & 190 & 200 \\  & & Sum & 137 & 210 & 347 \\ \hline \multirow{3}{*}{log(\(\lambda\))} & \multirow{3}{*}{Actual} & Signal & 122 & 25 & 147 \\  & & Noise & 10 & 190 & 200 \\  & & Sum & 132 & 215 & 347 \\ \hline \multirow{3}{*}{Multiple Features} & \multirow{3}{*}{Actual} & Signal & 136 & 11 & 147 \\  & & Noise & 10 & 190 & 200 \\ \cline{1-1}  & & Sum & 146 & 201 & 347 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Different methods test results. To further prove the effectiveness of this method, we selected one-year raw data of 2019 and used our method to detect these seismic signals. The process includes reading seismic array data, then performing beamforming processing, and applying our detection method to beam channel. After manual identification and confirmation, there are a total of 265 seismic signals associated with the seismic events that occurred in the specific area during this period. Our method proposed in this article detects 253 signals, and the signal detection rate is 95.5%, while STA/LTA detects 243 signals, and the detection rate is 91.7%, as shown in Table 7. We also found that the seismic signals missed by STA/LTA have lower SNR. Therefore, the application proved the effectiveness of our methods.