## 4 Experimental Design  
This research utilizes four statistical methods, all integrated into an advanced geospatial computational framework. These methods are employed to examine the temporal and spatial patterns of earthquake occurrences in the Red Sea region. For this study, we compiled comprehensive seismic data from both Saudi and Egyptian seismological networks, encompassing a range of time periods to develop an extensive seismicity catalog database. The Saudi dataset for this research is sourced from the Saudi Geological Survey (SGS), which has managed the Saudi National Seismic Network (SNSN). The SGS began deploying broadband seismic stations in 2004, and subsequently controlled the seismic stations from the King Abdulaziz City for Science and Technology (KACST) and King Saud University (KSU), following a directive from the Saudi Council of Ministers. Seismic data were collated from KSU (up to 2003), KACST (2004-2005), and SGS (2006-2020). The second pivotal dataset was acquired from the Egyptian National Seismological Network (ENSN), managed by the National Research Institute of Astronomy and Geophysics (NRIAG). The ENSN has played a crucial role in capturing a range of seismic events in the Red Sea area, documenting earthquakes with diverse magnitudes and characteristics. The ENSN's data, covering the period from 1998 to 2022, are integral to this study. The spatial configurations of seismic stations within both the SNSN and ENSN are detailed in Figures 1(a) and 1(c), respectively. Concurrently, the spatial distribution of the collected seismic events captured by these networks is illustrated in Figure 1(d). In our study, the seismic data from the southern section of the Red Sea, specifically below latitude 18-19 N, have been deliberately excluded from the final database. This decision was driven by the notable disparity in data availability between the two data sources of the Red Sea. As clearly illustrated in Figure 1(b), the Egyptian side of the Red Sea, encompassing the southern section, offered limited seismic data. In stark contrast, the Saudi side provided a more robust and comprehensive dataset. This uneven distribution of data led to the decision to exclude the southern section from our analysis, ensuring that our study was based on a dataset that was both reliable and adequately representative of the seismic activities we aimed to investigate. This methodological choice was essential to maintain the scientific rigor and accuracy of our findings, despite the geographical limitation it imposed. Table 1 offers a detailed statistical summary of the datasets from the ENSN and the SNSN. It includes important statistics like the total count, mean, standard deviation, and range for different temporal and spatial parameters of seismic events. These parameters cover the date and time of each event (year, month, day, hour, minute, second) and geographical details (latitude, longitude, depth, magnitude). Collecting these data shall help in understanding the distribution, central tendency, and variation of seismic activities within the mapped area. Based on the spatial distribution depicted in Figure 2 and the summary statistics presented in Table 1, it is evident that the collected ENSN and SNSN datasets closely align in terms of their sample sizes. Specifically, ENSN comprises 50,747 earthquakes, while SNSN encompasses 51,531 events. While the ENSN data are more current, covering the period from 1998 to 2022, SNSN offers a longer temporal span, ranging from 1988 to 2020. This is further substantiated by the average year of recorded events: ENSN's mean year is 2008, slightly more recent than SNSN's 2005 (Table 1), implying temporal variations in data collection. In spatial terms, both datasets encompass similar geographical extents. However, SNSN exhibits a broader distribution, as evidenced by its higher mean latitude of 27.64\({}^{\circ}\) and longitude of 35.01\({}^{\circ}\), compared to ENSN's mean latitude of 26.72\({}^{\circ}\) and longitude of 34.80\({}^{\circ}\).