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- Title
Combination of seismic attributes using clustering and neural networks to identify environments with sandstone-type uranium mineralization.
- Authors
Wu, Qubo; Li, Ziwei; Wang, Yanchun; Cao, Chengyin; Qiao, Baoping; Huang, Yucheng; Yu, Xiang
- Abstract
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.
- Subjects
URANIUM; URANIUM mining; SEISMIC networks; MINERALIZATION; DATA logging
- Publication
Acta Geophysica, 2023, Vol 71, Issue 6, p2715
- ISSN
1895-6572
- Publication type
Article
- DOI
10.1007/s11600-023-01044-1