Mahmoud El-Hadidy

The northern Part of Egypt comprises the southeastern part of the Eastern Mediterranean region. This region is characterized by its complex tectonic setting and earthquake activity. The current study represents the first attempt to investigate the crustal structure of Northern Egypt using Ambient Noise Tomography (ANT), utilizing local seismological data acquired by the Egyptian National Seismological Network (ENSN) broadband stations and regional stations deployed in southern Europe. The current study utilizes one year of continuous raw data recorded by 32 broadband stations, with interstation distances of 50–1400 km. About 300 dispersion curves are selected from a total of 496 based on signal-to-noise ratio (SNR) and the standard deviation of seasonal measurements. The accepted measurements are inverted into group velocity maps representing periods between (5–25 s). The results show lateral variations in group velocities across the region. The crust in the Northern part of the Western Desert is characterized by low velocity. In contrast, higher velocities are observed in the Northern Eastern Desert and the Sinai Peninsula. The Moho depth is calculated for nine sites across the study area. The estimated Moho depth ranges between 31 and 42 km. A relatively shallow Moho depth of 31–32 km is observed close to the southern Coast of the Mediterranean Sea and increases to 37–42 km in the middle of the Western Desert. Beneath the Sinai Peninsula, the Moho is located at a depth of 33–35 km. The Conrad discontinuity is noted at a depth ranging between 10 and 16 km.

The available historical documents for the city of Alexandria indicate that it was damaged to varying degrees by several (historical and instrumentally recorded) earthquakes and by highly destructive tsunamis reported at some places along the Mediterranean coast. In this work, we applied the neo-deterministic seismic hazard analysis (NDSHA) approach to the Alexandria metropolitan area, estimating ground motion intensity parameters, e.g., peak ground displacement (PGD), peak ground velocity (PGV), peak ground acceleration (PGA), and spectral response, at selected rock sites. The results of this NDSHA zonation at a subregional/urban scale, which can be directly used as seismic input for engineering analysis, indicate a relatively high seismic hazard in the Alexandria region (e.g., 0.15 g), and they can provide an essential knowledge base for detailed and comprehensive seismic microzonation studies at an urban scale. Additionally, we established detailed tsunami hazard inundation maps for Alexandria Governorate based on empirical relations and considering various Manning’s Roughness Coefficients. Across all the considered scenarios, the average estimated time of arrival (ETA) of tsunami waves for Alexandria was 75–80 min. According to this study, the most affected sites in Alexandria are those belonging to the districts of Al Gomrok and Al Montazah. The west of the city, called Al Sahel Al Shamally, is less affected than the east, as it is protected by a carbonate ridge parallel to the coastline. Finally, we emphasize the direct applicability of our study to urban planning and risk management in Alexandria. Our study can contribute to identifying vulnerable areas, prioritizing mitigation measures, informing land-use planning and building codes, and enhancing multi-hazard risk analysis and early warning systems.

A moderate size felt earthquake with a body wave magnitude (mb = 4.4) shocked the western part of northern Egypt on 11 April 2020. The nature of this seismic source has yet to be fully understood. This paper presents an analysis of the seismic source of this significant earthquake. A robust hypocentral parameters estimation is performed by collecting the data from 29 seismic stations in the Eastern Mediterranean region to reduce the azimuthal gap within the available location. The full-waveform Moment Tensor Inversion (MTI) is applied to derive the faulting mechanism of the quake. The obtained solutions indicate a reverse faulting mechanism. The plausibility of the obtained mechanism is analysed in terms of measuring the Kegan angle and evaluating the stability of the resulting mechanism. The source parameters are estimated using the S-wave amplitude displacement spectra. The obtained source parameters indicate that the seismic moment (Mo) is 8.9 × 10¹⁵ Nm, the fault radius L is 1013 m, the stress drop (σ) is 3.8 MPa, and the displacement (d) is 0.053 m. The analysis of this seismic source is of fundamental importance for evaluating seismic hazard in northern Egypt, particularly for critical facilities and safe hydrocarbon exploration.

The 1992 Cairo earthquake, with a moment magnitude of 5.8, is the most catastrophic earthquake to shock the Greater Cairo (GC) in recent decades. According to the very limited number of seismological stations at that time, the peak ground acceleration (PGA) caused by this event was not recorded. PGA calculation requires identification of nature of the earthquake source, the geologic setting of the path between the source and site under investigation and soil dynamic properties of the site. Soil dynamic properties are acquired by geotechnical investigations and/or geophysical survey. These two methods are costly and are not applicable in regional studies. This study presents an adaptive and reliable PGA prediction model using machine learning (ML) along with six standard geographic information system (GIS) interpolation methods (IDW, Kriging, Natural, Spline, TopoToR, and Trend) to predict the spatial distribution of PGA caused by this event over the GC. The model is employed to estimate the exposure of the urban area and population in the GC based on the available geotechnical and geophysical investigations. The exposure (population) data is from free and easy-access sources, e.g., Landsat images and the Global Human Settlement Population Grid (GHS-POP). The results show that Natural, Spline, and Trend are not suitable GIS interpolation techniques for generating seismic hazard maps (SHMs), while the Kriging Method shows sufficient prediction. Interestingly, with an accuracy of 96%, the ML model outperforms the classical GIS methodologies.