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首页> 外文期刊>Earth, planets and space: EPS >Thickness variation of the sedimentary cover in the South Western Desert of Egypt as deduced from Bouguer gravity and drill-hole data using neural network method
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Thickness variation of the sedimentary cover in the South Western Desert of Egypt as deduced from Bouguer gravity and drill-hole data using neural network method

机译：神经网络方法从布格重力和钻孔数据推算出埃及西南沙漠沉积覆盖层的厚度变化

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The Bouguer anomaly map of scale 1:500,000 and the lithological logs of more than 120 deep wells distributed in the Southern part of Western Desert of Egypt were used to determine the thickness of the sedimentary cover containing the main sandstone water formation. The predominant structures affecting both the basement rock and the sedimentary cover were also studied. Gravity stripping approach was applied to separate density anomalies within the sedimentary fill from the influence of deeper levels in the crystalline crust. The study indicated that the surface of the basement rock is highly rugged and mostly controlled by structures causing variation of the sedimentary cover thickness from location to other all over the area. Isopach maps were constructed based on the Artificial Neural Network (ANN) model which is considered a best method for that operation. The maximum thickness of sandstone formations is recorded at west Oweinat, southwest of Aswan, Dakhla oasis and west of Qena town. As this formation is the main water aquifer in the study area, therefore these locations are characterized by the presence of huge amount of ground water. Accordingly, these areas must be taking the priority in the programs of sustainable development in southern Egypt.
机译：使用规模为1：500,000的布格异常图和分布在埃及西部沙漠南部的120多个深井的岩性测井曲线来确定包含主要砂岩水层的沉积覆盖层的厚度。还研究了影响基岩和沉积盖层的主要结构。重力剥离法被应用来分离沉积物内部的密度异常，而不受晶体地壳深层影响。研究表明，基底岩石的表面非常崎，主要受结构控制，从而导致沉积覆盖层厚度从位置到整个区域变化。等值线图是根据人工神经网络（ANN）模型构建的，该模型被认为是该操作的最佳方法。砂岩地层的最大厚度记录在Oweinat西部，Aswan西南，Dakhla绿洲和Qena镇以西。由于该地层是研究区域的主要含水层，因此这些位置的特点是存在大量地下水。因此，这些地区必须在埃及南部的可持续发展方案中优先考虑。

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