Overview of the focus issue on ACAG8 of enhancing earthquake research through geomagnetic and seismic data analysis

The Arab Conference on Astronomy and Geophysics is a prominent biennial event that has been convening for 16 years. Hosted by the National Research Institute of Astronomy and Geophysics (NRIAG), the conference serves as a unique platform for presenting and discussing the latest advancements in astronomy and geophysics. Attended by representatives from Arab and international institutions, the conference facilitates knowledge sharing, collaborative research, and the dissemination of cutting-edge scientific findings. The 8th edition, held from October 9 to 12, 2023, brought together leading scientists, researchers, and academics from across the globe. Participants included keynote speakers, researchers presenting their studies, and attendees engaged in thought-provoking discussions, fostering an environment of collaboration and innovation. The primary goal of the conference is to spotlight innovative research in the fields of astronomy and geophysics, focusing on their applications in addressing societal challenges.

This focus issue consolidates select papers presented during the conference, focusing on the integration of geomagnetic, seismic, and deep learning methodologies to enhance earthquake prediction and understanding of tectonic processes.

Earthquakes remain one of the most destructive natural phenomena, and improving predictive models and hazard assessment strategies is essential for mitigating their impact. This focus issue highlights interdisciplinary research that leverages data from geomagnetic observatories, seismic monitoring stations, and machine learning algorithms to enhance our comprehension of earthquake mechanisms and crustal structures.

Aalaa Samy et al. (2025) present a study exploring the correlation between geomagnetic data recorded at Misallat (MLT) and Abu Simbel (ABS) observatories in Egypt and data from global INTERMAGNET stations. Their analysis reveals significant correlations during both quiet and disturbed geomagnetic events, reinforcing the reliability of Egyptian geomagnetic observatories in detecting earthquake precursors. The findings suggest that data from these observatories could fill critical gaps in the Middle East and North Africa, contributing to the refinement of regional earthquake models.

Mona H. Hegazi et al. (2025) introduce a novel capsule neural network for automating the selection of receiver functions (RFs) from seismic data in northern Egypt. This technique addresses the challenges of data volume and noise, streamlining crustal imaging and facilitating the study of subsurface discontinuities. The application of deep learning significantly enhances the efficiency and accuracy of RF analysis, advancing our understanding of crustal structures beneath seismically active zones.

Moataz Sayed et al. (2025) develop a 3D crustal density model of Egypt by integrating GOCE satellite gravity data with seismic profiles and borehole information. Their research identifies variations in Moho depth across Egypt, revealing geological correlations between crustal thickness and tectonic features. This model not only refines our knowledge of Egypt’s subsurface structures but also offers insights into the geodynamic processes shaping the region, contributing to more accurate seismic hazard assessments.

Mona Mohammed et al. (2025) propose the use of a ConvMixer neural network to predict peak ground acceleration (PGA) from seismic waveform data. By analyzing data recorded by seismic networks in Japan, their model demonstrates robust performance in predicting PGA immediately after earthquake onset, enhancing the capabilities of earthquake early warning systems (EEWS). The study highlights the potential of machine learning in providing real-time hazard assessments and improving the resilience of infrastructure.

The research presented in this issue underscores the importance of integrating geomagnetic and seismic data with advanced computational techniques to improve earthquake prediction and hazard mitigation. Moving forward, expanding geomagnetic observatory networks across the Middle East and Africa, coupled with the continued development of machine learning applications, holds promise for further advancements in seismic research.

This focus issue represents a collaborative effort to harness interdisciplinary approaches for enhancing earthquake resilience, underscoring the vital role of international cooperation in advancing geophysical research.