对沉积面层细分的可解释的人工智能
Andrea Di Martino1, Gianluca Carlini2, Alessandro Amorosi3
1Department of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna, Piazza di Porta San Donato 1, Bologna, 40126, Italy. Andrea.dimartino4@unibo.it.
Scientific reports
|January 22, 2026
概括
这项研究引入了一个可解释的AI管道,用于从核心图像中对沉积面部进行细分. 该框架提高了准确性,并提供了地质洞察力,架构了人工智能研究和地球科学应用的桥梁.
科学领域:
- 地质科学 地质科学
- 人工智能的人工智能
- 沉积物学的沉积物学
背景情况:
- 人工智能 (AI) 正在越来越多地改变地球科学.
- 可解释的人工智能 (XAI) 模型对于科学研究和实际采用至关重要.
- 准确的沉积面识别对于地质解释至关重要.
研究的目的:
- 开发和评估一条通用而又量身定制的管道,用于从全新世纪核心图像中语义分段沉积层.
- 使用可解释的人工智能技术增强模型的解释性.
- 展示XAI在地质科学中的价值,以实现透明的决策.
主要方法:
- 对卷积神经网络 (CNN) 和基于变压器的骨干进行评估,以获得最佳架构.
- 系统的补丁大小移除,以使模型推理与专家沉积物学知识保持一致.
- 显著性映射 (grad-CAM) 和预测的应用,以提高可解释性和不确定性量化.
主要成果:
- 开发的可解释的人工智能框架显著推进了自动面部预测.
- 该管道为模型行为和地质相关性提供了可操作的见解.
- Saliency 地图和预测有效地突出了有意义的地质区域,并量化了不确定性.
结论:
- 可解释的人工智能管道在地质科学中提供了实质性的附加值.
- 人工智能的透明决策对于桥梁研究和地质学的现实应用至关重要.
- 这种方法促进了人工智能在关键地质科学领域的整合.
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