分形自相似数据的稀疏恢复及其在铀分布中的应用

IF 1.5 3区化学 Q3 CHEMISTRY, ANALYTICAL

Journal of Radioanalytical and Nuclear Chemistry Pub Date : 2025-01-25 DOI:10.1007/s10967-024-09950-4

Jianyun Wang, Chenkai He, Zhenghua Xu, Yifan Chen, Yong Liu

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引用次数: 0

摘要

铀是一种稀有而重要的能源矿物。为了研究铀矿床的分布特征，建立了一种基于分形特征和稀疏已知点数据的数据恢复算法。该算法通过确定基函数、保持维数不变恢复和维数贪心优化等步骤，保证了恢复的准确性和分形特征的保留。通过数值试验与Kriging插值方法的比较，证明了该算法在恢复分形特征数据时精度误差和维数误差都有较好的改善。

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Sparse restoration of fractal self-similar data and its application in uranium distribution

Uranium is a rare and important energy mineral. In order to study the distribution characteristics of uranium deposits, we establish a data restoration algorithm with fractal features and sparse known point data. The algorithm ensures the accuracy of the restoration and the retention of fractal features through the steps of determining the basis function, preserving the dimension invariant restoration, and dimension greedy optimization. By comparing the numerical test with the Kriging interpolation method, it is proved that the algorithm is better in accuracy error and dimension error when restoring the fractal feature data.

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来源期刊

Journal of Radioanalytical and Nuclear Chemistry 化学-分析化学

CiteScore

2.80

自引率

18.80%

发文量

504

审稿时长

2.2 months

期刊介绍： An international periodical publishing original papers, letters, review papers and short communications on nuclear chemistry. The subjects covered include: Nuclear chemistry, Radiochemistry, Radiation chemistry, Radiobiological chemistry, Environmental radiochemistry, Production and control of radioisotopes and labelled compounds, Nuclear power plant chemistry, Nuclear fuel chemistry, Radioanalytical chemistry, Radiation detection and measurement, Nuclear instrumentation and automation, etc.