一种由视觉基础模型驱动的端到端故障解释方法,具有域调整微调的域调整微调
Yun Liu1, Fengyuan Zhang2,3, Huanyu Zou1
1Oil & Gas Pipeline Network Corporation, Shanghai, 200092, China.
Scientific reports
|November 25, 2025
概括
这项研究引入了一种新的AI框架,用于准确地解释地下气体储存中的地质断层. 它增强了地震数据分析,提高了能源基础设施的安全性和运营效率.
科学领域:
- 地质物理学 地质物理学
- 人工智能的人工智能
- 地质工程是地质工程.
背景情况:
- 准确地解读地质断层对于地下气体储存的安全性和效率至关重要.
- 手动口译是低效的,严重依赖于专家的经验.
- 当前的深度学习方法在有限的数据,不可靠的注释和缺乏地质物理原理方面扎.
研究的目的:
- 开发一个先进的人工智能框架,用于强大的地质断层解释.
- 在地震数据分析中克服现有的深度学习模型的局限性.
- 通过改进故障分析,提高地下气体储存操作的安全性和效率.
主要方法:
- 一个以模型驱动的视觉基础框架,具有域调整微调.
- 使用强化学习用于合成数据生成的故障意识自动增强.
- 不确定性驱动的自我注释优化用于可靠的注释循环.
- 地理学-受限特征对齐 微调,结合先前的地质知识.
主要成果:
- 显著提高了断层识别的稳定性,特别是在复杂的地质区域.
- 细分结果表明严格遵守地质原理和认知.
- 该框架提供了一个高效和可解释的智能解释范式.
结论:
- 拟议的框架有效地解决了数据稀缺性,注释不可靠性和地球物理原理集成问题.
- 它为地下气体存储的地质断层解释提供了一种优越的方法.
- 这一进步支持更好的顶岩完整性评估和断层密封分析.
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