一种基于变压器的岩石类型识别的新方法,通过利用声辐射来识别岩石类型
Tingting Wang1,2, Yifan Qin1, Ranjith P G3
1School of Electrical Engineering & Information, Northeast Petroleum University, Daqing, China.
PloS one
|August 27, 2024
概括
本研究介绍了3CTNet,这是一种新的深度学习模型,用于使用声辐射 (AE) 信号识别岩石类型. 该模型实现了高精度,改善了地质勘探和资源开发.
科学领域:
- 地球科学 地球科学 地球科学
- 地质物理学 地质物理学
- 人工智能的人工智能
背景情况:
- 传统的声辐射 (AE) 信号分析在岩石类型表征中的大数据方面面临着挑战.
- 使用深度学习进行智能岩石类型识别的有效特征提取仍然是发展的领域.
研究的目的:
- 提出基于AE信号的智能岩石类型识别的新型框架和深度学习模型 (3CTNet).
- 在处理复杂的AE数据时增强特征提取和模型性能.
主要方法:
- 开发了3CTNet,集成卷积神经网络 (CNN) 和转换器编码器用于AE信号分析.
- 采用自适应合成抽样 (ADASYN) 来平衡数据集并改善模型概括性.
- 将3CTNet的内部结构与现有的时间序列处理模型进行比较.
主要成果:
- 对于岩石类型,3CTNet模型实现了98.780%的高整体识别精度.
- 实验性比较验证了拟议的模型结构和特征提取方法的有效性.
- ADASYN显著提高了模型的稳定性和一般化能力.
结论:
- 3CTNet框架提供了一种高效准确的解决方案,用于使用AE信号进行智能岩石类型识别.
- 这种方法为地质勘探和石油/天然气开发提供了坚实的基础.
- 通过渐进的聚变提取先进的特征对于地球科学中的大数据分析至关重要.
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