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

• Autorzy: Wu, Qubo , Li, Ziwei , Wang, Yanchun , Cao, Chengyin , Qiao, Baoping , Huang, Yucheng , Yu, Xiang
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

sandstone-type uranium deposits; seismic attributes; neural networks; uranium-bearing environment; identifcation and prediction

• Czasopismo: Acta Geophysica
• Rocznik: 2023
• Tom: Vol. 71, no. 6
• Strony: 2715--2731
• Opis fizyczny: Bibliogr. 58 poz., rys., tab.

Twórcy

autor

Wu, Qubo

• School of Geophysics and Information Technology, China University of Geosciences, Beijing, 100083, China,
• CNNC Key Laboratory of Uranium Resource Exploration and Evaluation Technology, Beijing Research Institute of Uranium Geology, Beijing, 100029, China

autor

Li, Ziwei

• CNNC Key Laboratory of Uranium Resource Exploration and Evaluation Technology, Beijing Research Institute of Uranium Geology, Beijing, 100029, China,

autor

Wang, Yanchun

• School of Geophysics and Information Technology, China University of Geosciences, Beijing, 100083, China

autor

Cao, Chengyin

• CNNC Key Laboratory of Uranium Resource Exploration and Evaluation Technology, Beijing Research Institute of Uranium Geology, Beijing, 100029, China

autor

Qiao, Baoping

• CNNC Key Laboratory of Uranium Resource Exploration and Evaluation Technology, Beijing Research Institute of Uranium Geology, Beijing, 100029, China

autor

Huang, Yucheng

• CNNC Key Laboratory of Uranium Resource Exploration and Evaluation Technology, Beijing Research Institute of Uranium Geology, Beijing, 100029, China

autor

Yu, Xiang

• China Nuclear Geology, China National Uranium Corporation, Beijing, 100013, China

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Identyfikatory

DOI

10.1007/s11600-023-01044-1