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可靠和高效的磁性数据逆转用于资源检测,使用混合蝙蝠算法

Khalid S Essa¹, Mahmoud Elhussein², Omar A Gomaa¹

¹Geophysics Department, Faculty of Science, Cairo University, Giza, 12613, Egypt.

Scientific reports

|July 27, 2025

概括

本研究介绍了一种混合的蝙蝠算法 (BA) 和第四水平梯度 (FHG) 方法,用于从磁性数据中准确地估计地下参数. 这种方法有效地划分了地质结构,即使有噪音,也证明了地质物理勘探的实用性.

科学领域:

地质物理学地质物理学
地质物理勘探地质物理勘探
计算地质物理学计算地质物理学

背景情况:

准确的地下参数估计对于地球物理勘探至关重要.
解释磁场数据需要强大的方法来界定地下结构.
现有的方法可能会与噪音和区域场效应作斗争.

研究的目的:

开发和验证一种新的混合算法,以优化从磁性数据中估计几何参数.
将蝙蝠算法 (BA) 与第四水平梯度 (FHG) 集成,以增强地质物理逆转.
通过使用磁场数据来提高地下结构解释的准确性和分辨率.

主要方法:

混合算法结合了蝙蝠算法 (BA) 和第四水平梯度 (FHG).
FHG用于增强磁异常分辨率和减轻区域场效应.
BA 用于在优化几何参数 (深度,振幅,形状,起源,磁化角度) 中有效导航参数空间.
算法在合成数据集 (无噪声和10%高斯噪声) 和真实磁形数据上进行了测试.

主要成果:

混合BA-FHG算法准确地估计了地下的几何参数.
该方法在杂的环境中显示出强度和有效性.
使用来自法罗矿山综合体的真实磁性数据进行验证,显示与井数据和先前研究密切一致.
在地下参数估计中获得了更高的准确性和分辨率.

结论:

集成的BA-FHG方法在磁性数据集解释方面取得了重大进展.
该算法为理想化的几何模型提供可靠的地下参数估计.
这种方法通过提高反转精度和分辨率来提高地质物理勘探的实际有效性.

关键词:

蝙蝠的算法就是蝙蝠的算法.法罗矿是法罗矿的一个矿山.第四个水平梯度的梯度.地质物理勘探地质物理勘探磁性反转是一种磁性反转.地下表面成像的成像.

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