الفهرس 
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Abstract
Joint inversion of receiver functions (RFs) analysis and surface wave dispersion (SWD) is a powerful tool which can be applied to investigate accurately the crust and upper mantle seismic velocity structures. from around three decades this technique is considered as a successful and global method in constraining the velocity crustal structure model of a specific area. In this study, the northern Egypt is studied by using a combined inversion of body wave receiver functions and surface wave dispersion to provide constraints on the crustal structure. The two techniques are complementary to each other; the RFs are sensitive to the shear-wave velocity contrasts, while the SWD is sensitive to the finite variations of the shear wave velocity with depth. To enhance the resulted RFs beneath each station the H-k stacking method has been applied on a database of more than 122 teleseismic events to infer the lithosphere structure beneath northern Egypt. On the other hand, a joint inversion process between the resulted receiver function and the surface wave dispersion curves has been applied with the aim of resolving the non-uniqueness problem of the RFs. The obtained results indicate that, the crustal thickness differs from east to west and reaches its maximum value beneath Siwa station (SWA) in the north Western Desert and its minimum value beneath Sharm station (SHR) in southern Sinai. A clear image of the subsurface structure has been determined which shows that the crust of northern Egypt consists of three layers covered with a sequence of sediments that differs in thickness from site to another except in the Sharm area where the sedimentary cover is absent. Six shear-velocity models are deduced beneath the six seismic stations used in the study area with an average model for northern Egypt. To validate the obtained model, 58 seismic events in northern Egypt are relocated to assure and test how much its efficiency is. The new locations of such events showed a good clustering around the active faults and had minimum root mean square errors, (RMS) that indicate the good validity of our new model.