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深度学习和微分波技术用于伊朗塔罗姆的磁石-石勘探

Mohammad Mahdi Pourgholam¹, Ahmad Adib², Peyman Afzal¹

¹Department of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran.

Scientific reports

|August 29, 2025

概括

这项研究引入了基于融合的深度学习 (FDL) 方法来检测铁矿化. 该方法整合了各种勘探数据,以提高铁矿藏的矿产前景性.

科学领域:

地质科学
矿产勘探
数据科学

背景情况:

铁矿化对于了解铁沉积物的形成至关重要.
精确识别矿物化和原生成对于矿物勘探至关重要.
传统的方法往往难以整合不同的地质和地物理数据集.

研究的目的:

通过深度学习提出并验证一种用于检测铁矿的新方法.
整合多个来源的勘探数据以改善矿产前景绘制.
应用基于融合的深度学习 (FDL) 算法来识别潜在的铁矿区.

主要方法:

使用了包括遥感,空中地质物理,地化学和地质地图在内的综合探索数据集.
开发了一个基于提取的特征和类之间的逻辑关系的前性模型 (PM).
用于特征提取和值的微分波算法 (DWT,W-N,SA).
使用基于融合的深度学习 (FDL) 算法与预先训练的矿产前景测绘 (MPM) 网络.

主要成果:

通过使用GIS和前性模型成功集成多种数据集.
微分波算法有效处理和分类地质层.
通过FDL方法生成了矿物前景地图 (MPM),显示了潜在的铁矿区.
这项研究证明了深度学习在矿产勘探数据融合中的有效性.

结论:

拟议的基于融合的深度学习方法对检测铁酸盐矿化有效.
通过FDL整合多来源数据可以提高矿产前景测绘的准确性.
这种方法提供了一个强大的工具来识别新的铁沉积目标.

关键词:

勘探特征微分波形分类器基于深度学习的融合铁酸异常矿产前性地图展望性模型

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