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基于T-球形模糊信息,使用阿采尔-阿尔西纳聚合算子对矿山规划进行三维地震分析。

3D seismic analysis of mine planning using Aczel-Alsina aggregation operators based on T-spherical fuzzy information.

作者信息

Ma Lijun, Javed Kinza, Ali Zeeshan, Tehreem Tehreem, Yin Shi

机构信息

College of Land and Resources, Hebei Agricultural University, Baoding, 071000, China.

Department of Mathematics and Statistics, Riphah International University, Islamabad, 44000, Pakistan.

出版信息

Sci Rep. 2024 Feb 18;14(1):4001. doi: 10.1038/s41598-024-54422-0.

Abstract

3D seismic attributes analysis can help geologists and mine developers associate subsurface geological features, structures, faults, and ore bodies more precisely and accurately. The major influence of this application is to evaluate the usage of the 3D seismic attributes analysis in gold mine planning. For this, we evaluate the novel theory of complex T-spherical hesitant fuzzy (CTSHF) sets and their operational laws. Furthermore, we derive the CTSHF Aczel-Alsina weighted power averaging (CTSHFAAWPA) operator, CTSHF Aczel-Alsina ordered weighted power averaging (CTSHFAAOWPA) operator, CTSHF Aczel-Alsina weighted power geometric (CTSHFAAWPG) operator, and CTSHF Aczel-Alsina ordered.com weighted power geometric (CTSHFAAOWPG) operator. Some properties are also investigated for the above operators. Additionally, we evaluate the problems of 3D seismic attributes analysis to mine planning under the consideration of the proposed operators, for this, we illustrate the problem of the multi-attribute decision-making (MADM) technique for the above operators. Finally, we demonstrate some examples for making the comparison between prevailing and proposed information to improve the worth of the derived operators.

摘要

三维地震属性分析可以帮助地质学家和矿山开发者更精确、准确地关联地下地质特征、构造、断层和矿体。该应用的主要影响在于评估三维地震属性分析在金矿规划中的应用。为此,我们评估了复T-球面犹豫模糊(CTSHF)集的新理论及其运算规律。此外,我们推导了CTSHF阿采尔-阿尔西纳加权幂平均(CTSHFAAWPA)算子、CTSHF阿采尔-阿尔西纳有序加权幂平均(CTSHFAAOWPA)算子、CTSHF阿采尔-阿尔西纳加权幂几何(CTSHFAAWPG)算子和CTSHF阿采尔-阿尔西纳有序加权幂几何(CTSHFAAOWPG)算子。还研究了上述算子的一些性质。此外,考虑到所提出的算子,我们评估了三维地震属性分析在矿山规划中的问题,为此,我们阐述了上述算子的多属性决策(MADM)技术问题。最后,我们展示了一些示例,用于比较现有信息和所提出的信息,以提高所推导算子的价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c2c/11310451/6e8e3028ad3a/41598_2024_54422_Fig1_HTML.jpg

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