地震早期预警的虚拟场景,用于智能城市的灾害管理,基于辅助分类器生成对抗网络
Jae-Kwang Ahn1, Byeonghak Kim2, Bonhwa Ku3
1Earthquake and Volcano Technology Team, Korea Meteorological Administration, Seoul 07062, Republic of Korea.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
本研究介绍了一种AC-GAN模型,用于生成现实的地震场景,用于灾难应对培训,克服低地震性地区的数据限制. 生成的地震数据和强度指标与现实数据具有很强的相似性,有助于地震分析和预警系统.
科学领域:
- 地震工程的工程是地震工程.
- 灾害管理 灾害管理
- 人工智能的人工智能
背景情况:
- 有效地应对地震对于智慧城市灾害管理至关重要.
- 在低地震性地区培训数据有限,阻碍了对地震场景的模型开发.
- 创建现实的地震场景对于有效的灾难准备和应对培训至关重要.
研究的目的:
- 开发一种用于生成适用于任何地点的各种地震场景的新方法.
- 为应对地震应对模型培训数据不足的挑战.
- 通过先进的模拟技术增强灾害管理能力.
主要方法:
- 使用辅助分类器生成对抗网络 (AC-GAN) 来进行数据合成.
- 将辅助分类器纳入GAN的区分器,用于增强的场景生成.
- 创建了各种地震场景,包括地震数据和强度测量 (PGA,PGV,SA).
主要成果:
- 生成的地震数据表现出与实际钻孔传感器数据相似的特征.
- 与地面运动预测方程 (GMPE) 的比较验证了生成的强度测量 (IM).
- 产生的场景显示了地震预警培训的潜力.
结论:
- 该AC-GAN模型有效地产生多样化和现实的地震场景.
- 拟议的方法克服了数据稀缺问题,用于培训灾害响应模型.
- 这项技术显著提升了地震分析,检测管理和整体防灾准备.
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