基于痕和人工神经网络预测岩石断裂强度的综合方法
Wenhao Wang1, Shengyang Feng1, Xiaodong Wang2
1School of Resource Environment and Safety Engineering, University of South China, Hengyang, 421001, Hunan, China.
Journal of environmental radioactivity
|August 21, 2025
概括
预测岩石断裂的强度对于资源开采至关重要. 这项研究引入了人工神经网络 (ANN) 和追踪方法,在地质应用中达到90.7%的精度.
科学领域:
- 地质学
- 地质学
- 人工智能
背景情况:
- 对于资源 (石油,天然气,地热) 的勘探,精确地确定岩石裂变强度至关重要.
- 直接测量岩石断裂是技术上的挑战,阻碍了精确的强度评估.
研究的目的:
- 为预测岩石断裂强度制定综合方法.
- 使用人工神经网络 (ANN) 与追踪相结合,以提高预测准确度.
主要方法:
- 建立了一个迁移模型来模拟破碎岩石中的呼气率.
- 使用离散断层网络 (DFN) 模型生成的岩石断层.
- 用900个数字模拟数据集训练了一个ANN.
主要成果:
- 实现了高预测准确度,确定系数为0.907.
- 确定隐藏层和神经元的数量作为ANN模型准确性的关键因素.
- 通过预测突起的断裂强度来验证模型,显示与测量值的密切一致 (7.5%的差异).
结论:
- 综合ANN和追踪方法为预测岩石断裂强度提供了可靠的方法.
- 这种技术为克服地质和工程实践中直接破裂测量的局限性提供了可行的解决方案.
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