# Combining Higher-Order Statistics and Array Techniques to Pick Low-Energy P-Seismic Arrivals
## 5 Results
### False Detection Robustness: Comparison with STA/LTA  
As presented here, HOSA is a versatile algorithm that combines single-trace statistical analysis with multi-channel coherence techniques, making it a reliable tool for detecting low-energy seismic events. To provide a more comprehensive analysis, we conduct a peer-to-peer comparison with the widely used STA/LTA method in this section. STA/LTA [4] is traditionally used for single-trace triggering and detection tasks, particularly suited for continuous analysis. HOSA is designed for more refined analysis in networks with array geometry. The analysis of previous sections demonstrates HOSA's ability to handle low-energy P-wave arrivals and low-SNR signals, where STA/LTA often struggles. Hereafter, the robustness of HOSA in terms of false detections in noise-only traces is also analyzed and compared with STA/LTA performance. To further facilitate the peer-to-peer comparison, we focus on evaluating only the single-trace stage (STS) of HOSA, noting that the second multi-channel stage can further enhance the reliability of predictions. We analyzed two independent noise-only traces and made a comparison between the two techniques in terms of false detections. Figure 11 shows the analysis of Trace A, a trace extracted from one of the events used in our study at station 03 of array 01, while in Figure S1, Trace B from station 04 of array 02 is analyzed. Following the suggestion reported in [48], we set the parameters of STA/LTA in an appropriate way for detecting local weak earthquakes in a weak-motion context. Specifically, we set the following:  
1. Length of LTA windows to 15 s;
2. Length of STA windows to 0.5 s; \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**(a) Percentages of residual semblance values greater than 0.0**} \\ \hline  & HOSA & Catalog \\ \hline SNR \(<\) 4 & 46\% & 46\% \\
4 \(\leq\) SNR \(\leq\) 10 & 52\% & 44\% \\ SNR \(>\) 10 & 71\% & 55\% \\ \hline \multicolumn{3}{c}{**(b) Percentages of residual semblance values greater than 0.1**} \\ \hline  & HOSA & Catalog \\ \hline SNR \(<\) 4 & 26\% & 23\% \\
4 \(\leq\) SNR \(\leq\) 10 & 29\% & 21\% \\ SNR \(>\) 10 & 50\% & 26\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Percentages of residual semblance values greater than 0.0 (**a**) and greater than 0.1 (**b**) across varying SNR ranges. In each SNR interval, HOSA demonstrates better alignment onsets. For a detailed analysis, refer to Figure 10.  
Figure 10: Histograms of residual semblance for different SNR ranges. Bins have a width of 0.5. Each semblance value is computed by fixing an event and an array. Each panel contains semblance values of all the arrays whose mean SNR among traces falls in a fixed range (specifically SNR \(\leq\) 4; 4 \(<\) SNR \(<\) 10; SNR \(\geq\) 10). Bins corresponding to higher residual semblance values are consistently more populated for HOSA histograms than catalog ones across all SNR ranges. 3. Triggering threshold to 4;
4. Detriggering threshold to 2.5. HOSA settings remain the same as expressed in Table 3 of Section 4, except for \(\bar{t}\), which is set to 10 s here. In both Figures 11 and S1, the left panels (a and b) show the application of the STA/LTA technique. STA/LTA produces three false triggers for Trace A and three for Trace B. While higher threshold levels could reduce false detections, they would also lower detection rates in the context of microseismicity. We applied HOSA's STS for both Trace A and Trace B. To this end, we divided each trace analyzed with STA/LTA into five sub-traces of length equal to 10 s. We recall that STS searches for exactly one onset in only a sub-trace of 10 s and only accepts onsets whose standard deviations satisfy \(\sigma_{o}<75\) ms. The right panels of Figures 11 and S1 demonstrate that the STS of HOSA is more robust with respect to STA/LTA, producing only one single false detection among the five windows for Trace A and none for Trace B (other potential onsets have been discarded due to high \(\sigma_{o}\) values).