Paleoclimatic evolution of central Sudan during the Late Miocene to Pleistocene

In this paper, information derived from X-ray diffraction and heavy and light fractions analyses were discussed with the aim to trace the paleoclimatic changes of central Sudan during the Late Miocene to Pleistocene. Based on lithological and mineralogical characters noted in the Sayal and Umm Ruwaba Formations, four phases of distinct paleoclimatic changes were recognized. The first phase commenced in the Late Miocene during the deposition of the Sayal formation. The area was slightly uplifted and of gentle slope, a feature deduced from the deposition of clayey and fine-grained sandy materials with subordinate gravely component. A hot and humid climate, depicted from the development of kaolinite and iron oxide, is proposed during the deposition of the Sayal formation. The second phase is characterized by development of depressions in which alluvial streams and possibly small lacustrine basins occurred. This is inferred from the presence of sandy and silty materials, a characteristic of the lower and middle parts of the Umm Ruwaba Formation. The climate remained hot and humid during the deposition of the lower part of the Umm Ruwaba Formation in the early Pliocene. A shift to dry conditions with possible periodic humid seasons is, however, thought to be established during the deposition of the middle part of the Umm Ruwaba Formation deduced from the observed increase in salinity and decrease in iron oxide content. During the third phase, throughout the deposition of the upper part of the Umm Ruwaba Formation, the kinetic energy of streams increased as can be inferred from the presence of gravely intercalations. Deposition under arid climate is suggested for the lowermost part due to the increased amount of feldspars and the absence of iron oxide. However, evidence of cool condition is again noted at the topmost part of the formation inferred from the relatively high content of iron oxide in the deposits.     

Exploitation of TerraSAR-X Data for Land use/Land Cover Analysis Using Object-Oriented Classification Approach in the African Sahel Area, Sudan

Recently, object-oriented classification techniques based on image segmentation approaches are being studied using high-resolution satellite images to extract various thematic information. In this study different types of land use/land cover (LULC) types were analysed by employing object-oriented classification approach to dual TerraSAR-X images (HH and HV polarisation) at African Sahel. For that purpose, multi-resolution segmentation (MRS) of the Definiens software was used for creating the image objects. Using the feature space optimisation (FSO) tool the attributes of the TerraSAR-X image were optimised in order to obtain the best separability among classes for the LULC mapping. The backscattering coefficients (BSC) for some classes were observed to be different for HH and HV polarisations. The best separation distance of the tested spectral, shape and textural features showed different variations among the discriminated LULC classes. An overall accuracy of 84 % with a kappa value 0.82 was resulted from the classification scheme, while accuracy differences among the classes were kept minimal. Finally, the results highlighted the importance of a combine use of TerraSAR-X data and object-oriented classification approaches as a useful source of information and technique for LULC analysis in the African Sahel drylands.     

Statistical significance of modal parameters of bridge systems identified from strong motion data

Modal parameters of structural systems have commonly been determined using system identification (SI) methods for damage detection and health monitoring. For determining the deterioration of the integrity of structural systems correctly, modal parameters of a healthy structure have to be obtained with adequate certainty so that these parameters can be used as reliable references for the healthy system to compare with those of the damaged system. In this study, the statistical significance of modal parameters identified using strong motion time histories recorded on two bridge structures is assessed. The confidence intervals of identified modal frequencies and damping ratios are obtained using Monte Carlo simulations and sensitivity analyses in conjunction with eigenrealization algorithm. The dependence of the statistical bounds on model parameters is examined. The effect of using different number of sensors on the statistical significance is evaluated using simulated time history data from a validated finite element model of a bridge. Copyright © 2005 John Wiley & Sons, Ltd.     

The Red Sea depositional architecture: insights from 3D modeling

More than half a century of geological and exploration studies have taken place in the Red Sea area, and still very limited information is available to the geological community in regard to the lithological distribution and the stratigraphic architecture. In this study, extensive well data was used to build the first lithologic and stratigraphic 3D models of the entire Red Sea to better understand the lithological distribution. The potential models have been constrained by bathymetric and geophysical data. Studied data demonstrate that up to 5 km of sediments were deposited in the Red Sea. It is mainly comprised of limestones, evaporites, and shales. Our models show that the evaporite body represents more than 70% of the Red Sea succession. In particular, the evaporite succession seems to be well developed in the southern region. Salt dome features are present and developed close to the margins. The models suggest that domal formation did not enable thick carbonate accumulation in some parts of the basin but the carbonate generally follows the evaporite trend. The models help to identify the main controls leading to salt diapir by highlighting the distribution of this body and the geometry of geological structures. Syn-rift faulting and rifting has been one of the most prominent structural features. Complex interplay of tectono-stratigraphic events played a significant role in shaping the stratigraphic evolution of the Red Sea basin with multiple evolution phases of paleoenvironment and paleogeographic were recognized based on the models. Our synthesis and interpretation support that moderately deep marine conditions dominated in the Miocene, whereas shallow seas dominated the whole basin during the Plio-Pleistocene period as a result of episodic marine invasion. However, lacustrine environment may have prevailed at the Oligocene time in isolated half grabens.     

Kernel Principal Component Analysis for Efficient,Differentiable Parameterization of Multipoint Geostatistics

This paper describes a novel approach for creating an efficient, general, and differentiable parameterization of large-scale non-Gaussian, non-stationary random fields (represented by multipoint geostatistics) that is capable of reproducing complex geological structures such as channels. Such parameterizations are appropriate for use with gradient-based algorithms applied to, for example, history-matching or uncertainty propagation. It is known that the standard Karhunen–Loeve (K–L) expansion, also called linear principal component analysis or PCA, can be used as a differentiable parameterization of input random fields defining the geological model. The standard K–L model is, however, limited in two respects. It requires an eigen-decomposition of the covariance matrix of the random field, which is prohibitively expensive for large models. In addition, it preserves only the two-point statistics of a random field, which is insufficient for reproducing complex structures. In this work, kernel PCA is applied to address the limitations associated with the standard K–L expansion. Although widely used in machine learning applications, it does not appear to have found any application for geological model parameterization. With kernel PCA, an eigen-decomposition of a small matrix called the kernel matrix is performed instead of the full covariance matrix. The method is much more efficient than the standard K–L procedure. Through use of higher order polynomial kernels, which implicitly define a high-dimensionality feature space, kernel PCA further enables the preservation of high-order statistics of the random field, instead of just two-point statistics as in the K–L method. The kernel PCA eigen-decomposition proceeds using a set of realizations created by geostatistical simulation (honoring two-point or multipoint statistics) rather than the analytical covariance function. We demonstrate that kernel PCA is capable of generating differentiable parameterizations that reproduce the essential features of complex geological structures represented by multipoint geostatistics. The kernel PCA representation is then applied to history match a water flooding problem. This example demonstrates that kernel PCA can be used with gradient-based history matching to provide models that match production history while maintaining multipoint geostatistics consistent with the underlying training image.     

GIS-based estimation of flood hazard impacts on road network in Makkah city, Saudi Arabia

Makkah city, Saudi Arabia, is periodically exposed to flash floods that result in major human and economical damages. That is due to several factors including its rugged topography and geological structures. Hence, precise assessment of floods becomes a more vital demand in development planning. A GIS-based methodology has been developed for quantifying and spatially mapping the flood characteristics. The core of this new approach is integrating several topographic, metrological, geological, and land use data sets in a geographic information system (GIS) environment that utilizes the curve number method of flood modelling for ungauged arid catchments. Based on the estimated flood volume of sub-basins, a hazard factor has been developed to quantify the expected hazard level for each road. Applying this proposed approach reveals that 21?% of the road network in Makkah city is subjected to low flood hazards, 29?% is facing medium hazards, and 50?% of roads are exposed to harsh flood impacts. The developed approach may be considered a digital precise method that can be easily re-run, in other situations or regions, to estimate flood hazards on roads.     

A comparison between analytical aerial photogrammetry,laser scanning,total station and global positioning system surveys for generation of digital terrain model

The present paper compares between four data sources for creating digital terrain models (DTMs), based on analytical aerial photogrammetry, laser scanning, total station (TS) and global positioning system surveys. The case study presents the comparative results obtained using the methods listed above. After obtaining the ground coordinates of points using each method, DTMs were created using Surfer Software to establish comparison consistency. To check accuracy of each method used, a series of 100 independent points were collected at random positions over the case study area using the TS for horizontal position and a digital level for elevation. The comparison of the results of the methods was performed based on accuracy and efficiency aspects. The results have shown that TS and terrestrial laser scanners (TLSs) could represent precise, effective and rapid solutions to produce economical and accurate DTMs. Also, using TLSs allow more various possibilities of data representation.     

Temporal shoreline change and infrastructure influences along the southern Red Sea coast of Saudi Arabia

Jizan is one of the Saudi Arabian coastal cities endowed with diverse natural settings, which includes Ash Shuqayq in the north, Turfah in the centre and Jizan in the South. This work analysed specific environmental characteristics, such as spits, sabkhas and wadis. Assessments used Landsat imagery to examine coastal change between 1973 and 2011. The cumulative temporal change identified regression trends given by coefficients of determination that explained a significant percentage of data variation for Jizan (R ² = 69%) and Turfah (R ² = 72%), while Ash Shuqayq was insignificant (R ² = 14%). Inter-survey results predicted future change, although trends were not significant, i.e. Jizan (R ² = 22%), Turfah (R ² = 14%) and Ash Shuqayq (R ² = 3 and 61% with outlying value removed). Aerial photos showed regional coastal changes, which included a maximum accretion of 36.4 m and maximum erosion of 12.9 m. These are scientifically effective techniques to monitor regional coastal change, i.e. erosion and accretion and identified rates of 0.59, 1.80 and 3.53 myr^?1 for Ash Shuqayq, Turfah and Jizan. Changes were linked to infrastructure developments, e.g. tourism, port development and natural causes, e.g. spit formations and wadi outfalls.