打开黑盒子:可解释的人工智能用于核心和外露的自动化生物流分析
K Ayranci1, I E Yildirim2, E U Yildirim2
1College of Petroleum Engineering & Geosciences, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia. ayranci@gmail.com.
Scientific reports
|February 18, 2026
概括
可解释的人工智能 (XAI) 通过对模型决策提供视觉见解来增强地质学中的机器学习. 这项研究证实了XAI.
科学领域:
- 地质科学 地质科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 在地质学中越来越多地用于视觉解释任务,如岩石学分类.
- 机器学习模型的"黑盒子"性质阻碍了地质应用中的信任和解释性.
- 可解释的人工智能 (XAI) 通过为ML模型预测提供视觉解释来提供解决方案.
研究的目的:
- 用深度学习模型评估XAI在生物流强度分类方面的有效性.
- 评估XAI可视化与专家地质学解释的对齐性.
- 证明XAI在提高基于ML的地质分析透明度和可靠性的实用性.
主要方法:
- 预先训练的深度学习模型被用于图像分类.
- 该模型将地质图像分为三个生物流强度级别:没有生物流,中度生物流和强烈生物流.
- 应用了XAI技术来生成视觉热图,突出显示模型决策过程.
主要成果:
- XAI的可视化显示出与经验丰富的鱼类学家的解释有很强的一致性.
- 该研究证实了XAI提高ML驱动地质解释的透明度和可靠性的能力.
- XAI促进了一致和快速的图像评估,提高了效率.
结论:
- XAI是理解和信任地质科学中的ML应用的宝贵工具.
- XAI可以提高地质图像分析的效率和一致性.
- 在技术等专业领域,XAI具有作为教育资源的潜力.
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