深度学习可以预测全球地震引发的山体滑坡
Xuanmei Fan1, Xin Wang1, Chengyong Fang1
1State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China.
National science review
|June 16, 2025
概括
科学家们创建了一个全球山体滑坡数据库,并使用深度学习来预测全球地震引发的山体滑坡. 该工具提供快速,准确的危险评估,改善灾害应对和规划.
科学领域:
- 地质科学 地质科学
- 地理危险 地区性危险
- 计算地震学计算地震学
背景情况:
- 地震引发的山体滑坡是一个主要的致命危险,需要迅速应对,以防止连续发生的灾难.
- 目前的预测方法受到简化的模型,有限的区域数据和回顾性分析的阻碍,阻碍了及时的危险评估.
- 有效地质危险预测需要全面的全球数据和先进的分析技术.
研究的目的:
- 开发一个全面的全球地震引发的山体滑坡数据库.
- 创建先进的深度学习模型,以快速,准确地预测地震后的山体滑坡概率.
- 为即时灾难评估和事前危险规划提供一个可扩展的工具.
主要方法:
- 编制了一个全球数据库,记录了过去50年来38次大地震造成的约40万次山体滑坡.
- 开发并应用先进的深度学习模型来预测山体滑坡概率.
- 已验证的模型性能,用于快速的全球危险评估.
主要成果:
- 在山体滑坡预测中达到约82%的平均空间准确度.
- 在不到一分钟的时间内预测任何全球地震的山体滑坡概率.
- 在危险评估中成功绕过了先前当地知识的需求.
结论:
- 开发的框架为全球地缘危险预测提供了转型性的进步.
- 能够快速评估灾害,并加强地震引发的山体滑坡的事前危险规划.
- 提供了一个可扩展和有效的工具,以减轻地震滑坡的灾难性影响.
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