# Combining Higher-Order Statistics and Array Techniques to Pick Low-Energy P-Seismic Arrivals
## 6 Discussions and Conclusions
The results of the HOSA algorithm support the possibility to refine and eventually increase the dataset of phase arrival times provided by advanced detection techniques. Recent studies pointed out limitations in the application of machine-learning pickers, including EQTransformer, for the identification of the phase arrival times due to fluctuations of the probability values according to the different positions of the earthquake within the considered portion of continuous data [29; 49]. Increasing the overlap between adjacent time windows has been proposed as a mitigation strategy to improve the robustness of detections made by machine-learning pickers [29; 49], leading to a higher computational cost when applying continuous data. Therefore, HOSA is also presented as an integrating strategy to effectively complement the application of machine-learning pickers. HOSA does not require any special technological infrastructure. In fact, differently from other automated approaches, HOSA is not time-consuming and computer-demanding and does not require large memory allocations (it takes less than 5 min to process \(\simeq\) 30,000 traces on a Workstation DELL Precision 7820 Tower with 2 Intel Xeon Silver 4214R 2.4 GHz, and 12 \(\times\) 8 GB DDR4 2933 RDIMMECC). For this reason, HOSA appears to be remarkably suitable for potential employment in automated workflows. The reliable predictions generated by HOSA make it suitable for efficiently revising or validating large volumes of data in catalogs using a consistent method. It can quickly and robustly discard incorrect P-onsets and refine inaccurate ones in databases. Moreover, HOSA can ideally integrate automatic real-time procedures for the picking of the seismic phases, such as those routinely adopted for seismic monitoring, improving the quality of the readings and eventually allowing a more precise automatic earthquake location. In conclusion, we found that HOSA solutions have the following characteristics:  
1. They are reliable and less prone to false P-onset detection than STA/LTA;
2. They show higher inter-array consistency than template-matching solutions, especially in cases of a high number of detected onsets per array. Future developments of HOSA could include tailoring of method for more challenging scenarios, such as picking secondary seismic phases or emergent arrivals from volcanic or tectonic tremors. Moreover, additional enhancements in detection reliability could be investigated by testing the MCS, which leverages array-based information, to improve false detection robustness further. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15031172/s1](https://www.mdpi.com/article/10.3390/app15031172/s1)]([https://www.mdpi.com/article/10.3390/app15031172/s1](https://www.mdpi.com/article/10.3390/app15031172/s1)). The file includes Figure S1: Trace B analysis--Robustness to false detection for STA/LTA and single-trace stage (STS) of HOSA; Figure S2: Zoomed-in views of HOSA's application (STS) on the sub-traces from Trace A (Figure 11 in the main text). Conceptualization: G.M. and M.P. ; methodology, G.M. and M.P. ; software: G.M. and M.P. ; validation: G.M., S.S., F.N., and O.A. ; formal analysis: G.M. ; data curation: F.N. and F.S.d.U. ; writing--original draft preparation: G.M., S.S., F.N., O.A., M.P., and F.S.d.U. ; writing--review and editing: G.M., S.S., F.N., O.A., M.P., and P.C. ; visualization: G.M. and O.A. ; supervision: [PERSON] and S.S.; project administration: P.C. ; funding acquisition: O.A. All authors have read and agreed to the published version of the manuscript. This work was partially supported by TOGETHER-Sustainable geothermal energy for two Southern Italy regions: geophysical resource evaluation and public awareness financed by European Union--Next Generation EU Piano Nazionale di Ripresa e Resilienza Missione 4-Component 2-CUPD53D23022850001 and by a PhD fellowship financed by the European Union--Next Generation EU DM 118/2023, CUP D42B23001820006.