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在岩石样本中测量断裂网络,使用X射线断层扫描,卷积过和深度学习
Alessia Caputo1, Maria Teresa Calcagni1, Giovanni Salerno1
1Department of Industrial Engineering and Mathematical Sciences, Polytechnic University of Marche, 60131 Ancona, Italy.
这项研究引入了一种使用X射线计算机断层扫描 (CT) 和神经网络的新方法,以准确检测地质样本中的复杂骨折. 该方法增强了对具有挑战性的岩层形成的断裂识别,改善了地质分析.
科学领域:
- 地质地质地质地质地质地
- 图像分析 图像分析
- 人工智能的人工智能
背景情况:
- 在地质样本中检测断裂对于理解岩石力学和资源勘探至关重要.
- 传统方法在X射线计算机断层扫描 (CT) 数据中常见的低对比度,噪声和文物方面扎.
- 不同质的地质材料,如马尔石灰岩,对断裂特征提出了独特的挑战.
研究的目的:
- 开发和验证一种可靠的方法来检测和描述地质样本中的断裂,使用X射线CT.
- 提高断裂网络识别和量化的精度和可靠性.
- 解决CT系统限制和地质样本特征引起的不确定性.
主要方法:
- 集成基于卷积的图像处理与基于神经网络的细分用于骨折检测.
- 应用预处理技术,包括高斯过和中位过,多角度扫描和强度正常化.
- 在意大利Maiolica形成的marly石灰岩样本上进行验证,该岩石以复杂的断裂模式而闻名.
主要成果:
- 拟议的方法在识别复杂的断裂网络方面实现了高精度,优于传统的基于卷积的方法.
- 神经网络细分有效地区分了从宿主岩石中填充石或粘土的骨折.
- 该工作流显著提高了在具有挑战性的CT数据中骨折量化的可靠性和准确性.
结论:
- 结合CT和神经网络的方法提供了一个强大的和可重复的框架来分析复杂地质材料中的不连续性.
- 这种方法增强了对地质上重要的形成中断裂系统的理解.
- 该研究为准确的断裂量化提供了可靠的工具,对各种地质科学应用至关重要.
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