放射性地质物理处理和空间异常分析基于自适应加权融合和多分形SVM
Wenjie Lv1, Pei Huang1, Yaxin Yang2
1School of Geophysics and Measurement-control Technology, East China University of Technology, Nanchang, 330013, China.
Scientific reports
|July 2, 2025
概括
这项研究将多个放射性勘探数据使用自适应加权进行融合,以更好地检测异常. 增强的数据集改善了地质物理特征的反映,并有助于预测有利的勘探目标区域,准确度为82.7%.
科学领域:
- 地质物理学 地质物理学
- 地质化学 地质化学
- 数据科学数据科学数据科学
背景情况:
- 单个数据集限制了放射性勘探中的全面异常检测.
- 整合多样化的数据源对于准确的地质物理解释至关重要.
研究的目的:
- 开发一种适应性加权聚变方法,用于组合多个放射性勘探数据.
- 增强异常信息,改善对有利的勘探目标地区的预测.
主要方法:
- 适应加权融合地面马射线光谱,热发光和210Po活动数据.
- 聚焦区域 (C-A) 多分形模型用于对监督学习标签进行分类.
- 支持矢量机 (SVM) 用于预测有利的勘探目标区域.
主要成果:
- 适应融合创造了更全面的数据集,有效地反映了异常放射性特征.
- 在有利的勘探目标地区,SVM模型实现了82.7%的预测准确度.
- 建立了一个模型来分析异常放射性矿石体的空间分布,并推断深层目标.
结论:
- 适应加权聚变显著改善了放射性勘探数据分析.
- 综合方法提高了深层放射性矿石体的识别和预测.
- 这种方法为单一方法映射提供了更有效和更全面的替代方案.
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