Investigation of Groundwater Potential Zones in Hard Rock Terrain,Wadi Na'man,Saudi Arabia

In arid and hyper-arid zones, groundwater exploration is one of the most significant ways to locate potential new water supplies. Geophysical prospecting is currently the most successfully used method for locating new supplies, but it is rather costly. Satellite remote sensing (RS) detection, however, with its integration of Geographic Information Systems (GIS) provides the best chance for identifying and initially evaluating water-bearing formations. In the western part of Saudi Arabia, Wadi Na'man has for centuries been one of the major water sources for the city of Makkah Al-Mukarramah. It is therefore very important to find appropriate groundwater potential (GP) zones in this wadi for water supply. This study utilizes RS and GIS techniques, and also studies the hydrogeological, geological, and geomorphological characteristics that have significant impact on groundwater occurrence in Wadi Na'man. Representative layers are generated for each component and each given a weight ratio that depends on the level of influence. The overlay and integration of these thematic layers was used to produce a map that shows the most promising potential groundwater areas and classifies local potentials as either low, medium, or high. The results also reveal that the areas overall-rated as “promising” (i.e., classified as medium or high) represent approximately 17-25% of the total basin area and consist mainly of Quaternary sediments and connected fractured rock areas.     

Dimensionality‐reduced estimation of primaries by sparse inversion

Wave‐equation based methods, such as the estimation of primaries by sparse inversion, have been successful in the mitigation of the adverse effects of surface‐related multiples on seismic imaging and migration‐velocity analysis. However, the reliance of these methods on multidimensional convolutions with fully sampled data exposes the ‘curse of dimensionality’, which leads to disproportional growth in computational and storage demands when moving to realistic 3D field data. To remove this fundamental impediment, we propose a dimensionality‐reduction technique where the ‘data matrix’ is approximated adaptively by a randomized low‐rank factorization. Compared to conventional methods, which need for each iteration passage through all data possibly requiring on‐the‐fly interpolation, our randomized approach has the advantage that the total number of passes is reduced to only one to three. In addition, the low‐rank matrix factorization leads to considerable reductions in storage and computational costs of the matrix multiplies required by the sparse inversion. Application of the proposed method to two‐dimensional synthetic and real data shows that significant performance improvements in speed and memory use are achievable at a low computational up‐front cost required by the low‐rank factorization.