The Red Sea region, situated between the Arabian and African Plates, experiences significant seismic activity due to its tectonic dynamics, with earthquakes ranging from minor to potentially destructive events. This study aims to develop smoothed seismicity models for the region by using an enhanced seismic catalog specific to the Red Sea. This facilitates a detailed spatial and temporal analysis of seismic events, focusing on seismic source characterization essential for probabilistic seismic hazard assessments. A rigorous declustering method excludes foreshocks and aftershocks, focusing on independent seismic events. The analysis uses a spatial grid (0.1 $$^{\circ }$$∘ cells in latitude and longitude) to determine seismic event rates, which are then refined using various smoothing techniques. Special attention is given to seismic activity within 0–35 kms of depth, leading to distinct rate models that inform urban development and seismic hazard mitigation strategies in the Red Sea area. These models are crucial for improving resilience, safety, and informed decision-making for urban planning and disaster preparedness, addressing the challenges posed by the region’s tectonic and seismic complexities.

A comprehensive geostatistical analysis was conducted on a dataset comprising 24,321 seismic events in the Red Sea region, spanning from 1997 to 2020. This analysis involved the creation of a new seismic activity database, incorporating data from both Egyptian and Saudi Seismic Networks. This enriched database provided a robust foundation for a detailed examination of the seismic patterns and activities in the region. Utilizing geographic information systems and various spatial analytic methods, it identifies seismic patterns and tectonic influences. The findings reveal significant seismic clustering along the Central Red Sea axis, indicative of active rifting between the Nubian and Arabian plates. The study demonstrates spatial autocorrelation in seismic activities, with high-high clusters marking zones of elevated seismicity. Kernel Density Estimator analyses highlight concentrated seismic activity in the Gulfs of Aqaba and Suez. Higher magnitude events are shown to localize in areas of greater tectonic stress, aligning with known geological features. This research provides critical insights into the seismic dynamics of the Red Sea, showcasing the effectiveness of geostatistical techniques in analyzing seismic data in tectonically active regions.

This investigation concentrates on refining the accuracy of earthquake parameters as reported by various Saudi seismic networks, addressing the significant challenges arising from data discrepancies in earthquake location, depth, and magnitude estimations. The application of sophisticated machine learning techniques, particularly the Isolation Forest algorithm, has markedly enhanced the precision in the estimation of seismicity parameters by effectively identifying and eliminating outliers and discrepancies. A newly developed and refined seismicity catalog was employed to accurately determine key seismic parameters such as the magnitude of completeness ($M_c$), a-value, and b-value, thereby underlining their indispensable role in regional seismic hazard assessment. The research underscores the substantial impact of data inconsistencies on the evaluation of seismic hazards, thereby advocating for the advancement of research methodologies within the field of seismotectonics. The insights derived from this study significantly contribute to a more profound understanding of the seismotectonic processes in the region. These insights are crucial for the development of comprehensive seismic hazard assessments and the formulation of targeted earthquake preparedness strategies, thereby enhancing resilience against seismic risks in the region.