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2 Acta Geophysica

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Two-dimensional hydrodynamic modeling for prediction of bank erosion and bed incision in the Indus River

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Boota M. W. , Yan C. , Soomro S.-e. h. , Zafar M. A. , Li Z. , Xu J. , Yousaf A.

tom Vol. 72, no. 3

2041--2058

The Lower Indus River (LIR) in the Southern Sindh has experienced by multiple measurable changes in its planform and longitudinal profiles over the last 100 years. This research deals with a hydrodynamic model coupled with rough set theory (RST) model findings that accounts for the prediction of lateral and vertical morphodynamic evolution observed over the 32 km reach during the flood episode of 2020. Human interferences and hydrodynamic aspects during high flood periods were assessed in the context of channel morphology. Surveyed cross-sections were used to construct the geometry using two-dimensional (2D) Hydrologic Engineering Center's River Analysis System (HEC-RAS) model, and simulation was completed under the unsteady flow values among the highest runoff and bankfull values. The island and natural bend of the river have higher values of velocities and shear stresses, and consequently higher erosion and incision rate was observed. The bank erosion was computed with high precision (R2 = 0.83) based on improved connection of erodibility coefficient and excess shear stress technique. The present study findings will be helpful to assist in the implementation of river protection works at the given locations of Indus River and will serve as a framework for similar river reaches.

Combination of seismic attributes using clustering and neural networks to identify environments with sandstone-type uranium mineralization

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Wu Q. , Li Z. , Wang Y. , Cao C. , Qiao B. , Huang Y. , Yu X.

2023

tom Vol. 71, no. 6

2715--2731

How to effectively and economically estimate the occurrence of sandstone-type uranium deposits in deep metallogenic environments considering the high costs of drilling methods has troubled the uranium geologists for a long time. To address this, we developed a novel workflow using a combination of clustering and neural networks in seismic attributes (SA) analysis to characterize uranium-bearing environments in directly predicting uranium zones. In the workflow, PCA and a clustering method for SA analysis in the target layer were first used to predict favorable zones for uranium; second, supervised neural networks were utilized for quantitative estimation of gamma volume, with subsequent delineation of the zones with high gamma anomalies; finally, results from the two processes were examined for mutual intersection, and intersection results with ordinal ranking of A and B were then extracted. In practical application, several A-level and B-level regions were predicted, which represent very high U-mineralization potential and significant potential, respectively. The prediction results were then essentially verified by logging data of the study area. We consider our developed approach to be a reasonably cost-effective technique for uranium-bearing environment prediction as it can yield useful suggestions for drilling programs with significantly reduced cost and prospecting risk.