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一个混合本地-全球特征关注网络,用于薄片岩石图像分类.
Peiyang Wei1,2,3, Changyuan Fan4, Xiwen Yang5
1School of Software Engineering, Chengdu University of Information Technology, Chengdu, 610225, China.
Scientific reports
|January 28, 2026
概括
新的深度学习模型HFANet通过结合本地纹理和全球背景,准确地对岩石薄截面图像进行分类. 这一进步通过增强的岩石学分类来改善地质调查和资源勘探.
科学领域:
- 地质科学是地球科学.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 精确的岩石薄截图图像的分类对于地质应用至关重要.
- 当前的深度学习模型面临着在平衡局部纹理和全球背景的挑战,以识别细粒度岩石.
研究的目的:
- 开发一种新的混合网络,HFANet,用于改进薄截面岩石图像分类.
- 加强本地和全球特征的整合,以实现更准确的岩石学分类.
主要方法:
- 拟议的HFANet是一个混合网络,集成DenseNet (本地) 和Swin Transformer (全球) 分支.
- 实现了多头自我注意和双向交叉注意,用于功能交互和指导.
- 利用集体分类框架与手工地质特征的自适应融合和多式融合.
主要成果:
- 与岩石薄截面数据集上最先进的模型相比,HFANet实现了更高的性能.
- 在沉积物子集上达到高达99.03%的准确性和完美的AUC/AUPR得分.
- 废弃研究和可解释性分析验证了模型组件的有效性,并专注于地质特征.
结论:
- HFANet有效地平衡了本地和全球特征,以准确地进行石质学分类.
- 该模型显示了推进智能地质科学图像分析的巨大潜力.
- 在自动化地质分析和资源勘探中展示了实际应用.
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