On 12 November 2017, an earthquake occurred in Sarpol Zahab city, located on the Iraq/Iran boundary, with a moment magnitude (Mw) of 7.3. Advanced Land Observing Satellite 2 (ALOS-2) L-band (23.6 cm wavelength) and C-band Sentinel-1A data (ascending and descending) were used to detect the co-seismic displacements maps caused by this earthquake. The ALOS-2 data was utilized to reconstruct the 3D co-seismic displacements maps, as well as estimate the fault-dip and slip distribution along the rupture. The results showed the maximum surface displacement in the north, east, and up directions to be 100, 50, and 100 cm, respectively. The best-fit faulting geometry had a strike of 337.5° and a dip of 11.2° toward the northeast, at a depth of 8 km. The predicted geodetic moment was 1.15 1020 Nm, which corresponds to a magnitude of Mw 7.31. There were two significant slip sources: one in the shallower depth range of 8.5–10 km, with a peak slip of 5 m, and another in the depth range of 10.5–20 km, with a peak slip of 5.3 m. Both controlled the principal deformation signals in geodetic images. The slip was concentrated, along with a strike distance of 20 to 40 km, at a depth of 10 to 20 km. The earthquake was caused by the Zagros Mountains Front Fault (ZMFF), based on the results of 3D co-seismic deformation, inferred slip, preliminary investigation, and interpretation of the mainshock, as well as aftershock distributions.

In geotechnical engineering, in-situ tests, like standard penetration test (SPT) and cone penetration test (CPT), are essential to evaluate the underground conditions. SPT is a frequently used geotechnical approach, while CPT is considered one of the best exploration apparatuses. However, there is a demand for a correlation of SPT-CPT that can be used in the geotechnical design. This research aimed to find the relationships between the standard penetration test’s blow count (N) and cone penetration test’s tip resistance (qc) for different groups of sandy soils in Port Sudan city, Sudan. The data used in this study was attained from numerous projects conducted in the city to identify the underground conditions. The values of the SPT and CPT tests were compared and tried to generate specific statistical data. The correlations were established between qc and N for various soils. Uphill linear relationships wear found between qc and N for different sandy soils. These results were interpreted based on their correlation coefficients. The study proposed formulas to predict qc from N for three soil groups, with a high correlation coefficient. Besides, qc/N ratios were calculated for different soil groups; the current research results agree with those in the published literature with less high values.

Although the cone penetration test (CPT) is essential for geotechnical design, it is often not economically feasible and time-consuming to perform the tests at all sites. This work used data from Port Sudan City to create a model to predict CPT values from Standard Penetration Test (SPT) attributes using a Support Vector Regression (SVR) algorithm. This approach converted the SPT into attribute combinations through a stepwise regression analysis. Subsequently, the attributes were converted to CPT by training the SVR neural network with the available CPT data. Cross-validation was performed to assess the credibility of the SPT parameters in the CPT transformation. In this procedure, a quarter of the data set is omitted from the training set each time, and the transformation is recalculated. Next, the accuracy of the transformation in estimating CPT from the deleted data is evaluated; this procedure is applied to all data in the training set. The results show that comparing the actual and predicted CPT gives a good agreement, as indicated by high correlation coefficient values for the training and test data sets. This approach can be used for automated estimation of CPT from SPT parameters and can serve as an extension of conventional techniques.