强震动地震学与人工智能：进展与展望

陈苏; 傅磊; 丁毅; 刘献伟; 李多为; 胡晓虎; 李小军

doi:10.11899/zzfy20250165

强震动地震学与人工智能：进展与展望

doi: 10.11899/zzfy20250165

陈苏^{1, 2,},
傅磊²,
丁毅¹,
刘献伟²,
李多为³,
胡晓虎¹,
李小军^{1, 2, ,}

1.
北京工业大学, 城市与工程安全减灾教育部重点实验室, 北京 100124
2.
中国地震局地球物理研究所, 北京 100081
3.
南洋理工大学, 土木与环境工程学院, 新加坡 639798

基金项目: 国家重点研发计划项目（2023YFC3007403）；国家自然科学基金（52192675、51878626）

详细信息

作者简介:
陈苏，男，生于1986年。教授，博士生导师。主要从事工程地震与人工智能交叉工作。E-mail：chensuchina@126.com

通讯作者:
李小军，男，生于1965年。教授，博士生导师。主要从事工程地震方面的研究工作。E-mail：beerli@vip.sina.com

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出版历程
- 收稿日期: 2025-09-21
- 录用日期: 2025-10-27
- 修回日期: 2025-10-21
- 网络出版日期: 2025-11-07

AI for Engineering Seismology: Advances and Prospects

Chen Su^{1, 2
,},
Fu Lei²,
Ding Yi¹,
Liu Xianwei²,
Li Duowei³,
Hu Xiaohu¹,
Li Xiaojun^{1, 2
, ,}

1.
Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China
2.
Institute of Geophysics, China Earthquake Administration, Beijing 100081, China
3.
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore 639798

摘要

摘要: 数十年间，地震学家及地震工程学家通力合作，为包括地震构造特征、地震活动性、震源特性、地震动预测模型及场地效应等多个关键问题的解决提供了支撑，形成了地球科学与工程科学交叉融合的具有独特性的工程地震学科，并取得了系统的应用性研究成果。作为工程地震学重要分支的强震动地震学得到了迅猛发展，为地震区划和工程抗震研究奠定了坚实基础，为城乡建设和核电、交通、能源等多类型行业的发展提供了地震安全保障。近年来，随着算力、算法及算料（数据）等人工智能关键要素的大力发展，进一步实现强震动地震学与信息学科交叉成为可能，也迅速成为本领域的热点问题。本文首先分析了强震动地震学研究进展与关键问题，探讨了其与人工智能交叉的框架。而后从知识嵌入、数据-知识融合及知识发现3个层面，综述了行业研究成果，重点介绍：（1）地震波动相关的控制方程与边界、初始条件物理嵌入理论与求解方法；（2）数据与物理机制联合驱动的人工智能地震动模型构建理论与方法；（3）强震动人工智能生成模型等。最后，讨论了目前强震动地震学与人工智能研究亟须解决的关键问题，并对未来的发展方向进行了展望。
- 强震动地震学 /
- 人工智能 /
- 学科交叉 /
- 知识嵌入 /
- 知识发现
Abstract: Over the past few decades, seismologists and earthquake engineers have worked in close collaboration, jointly addressing a range of critical issues, including seismotectonic characteristics, seismicity, source properties, ground-motion modeling, and site effects. These efforts have led to the establishment and maturation of engineering seismology, a distinctive interdisciplinary field bridging Earth sciences and engineering sciences, and have produced a series of systematic and impactful applied research outcomes. Among its subdisciplines, strong-motion seismology has developed particularly rapidly, providing a solid scientific foundation for seismic zonation and earthquake-resistant engineering, and playing a crucial role in ensuring seismic safety for urban and rural construction as well as for key sectors such as nuclear power, transportation, and energy. In recent years, rapid advances in artificial intelligence—especially in computing power, algorithms, and data availability—have created new opportunities for deep integration between strong-motion seismology and information science, making this interdisciplinary direction a major focus of current research.This paper first reviews recent progress and outstanding challenges in strong-motion seismology and outlines a conceptual framework for its integration with artificial intelligence. It then surveys representative studies from three perspectives: knowledge embedding, data–knowledge fusion, and knowledge discovery, with emphasis on the following topics: (1) theoretical foundations and solution strategies for physically embedding governing equations, boundary conditions, and initial conditions associated with seismic wave propagation; (2) theories and methodologies for developing artificial-intelligence-based ground-motion models driven by the joint constraints of observational data and physical mechanisms; and (3) artificial-intelligence-based models for strong-motion simulation and generation. Finally, the paper discusses key issues that urgently need to be addressed in current interdisciplinary research between strong-motion seismology and artificial intelligence, and provides perspectives on future development directions.
- Engineering seismology /
- Artificial intelligence /
- Interdisciplinary studies /
- Knowledge embedding /
- Knowledge discovery

HTML全文

图 1 强震动地震学与人工智能的研究框架

Figure 1. The framework of engineering seismology and artificial intelligence

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图 2 不同GMM的标准差及其对应年份（Strasser等，2009）

Figure 2. The standard deviations of different GMMs and their corresponding years

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图 3 基于生成对抗网络的地震动生成模型框架（陈苏等，2026）

Figure 3. A framework for artificial seismic data generation based on generative adversarial networks

下载: 全尺寸图片幻灯片

表 1 已有研究针对PINNs波动建模的关键问题与技术手段

Table 1. Key issues and technical approaches in wave modelling using PINNs addressed by existing research

关键问题	技术手段	时频类型	文献来源
点源奇异性	在解析的背景波场的基础上求解散射波场	频域	Alkhalifah等（2021）；Song等（2021）
	将早期初始波场作为初始条件	时域	Moseley等（2020）；Rasht-Behesht等（2022）；Ding等（2023a）
	以平滑的高斯函数定义空间分布的方式注入源	时域	Zhang等（2023）；Sethi等（2023）；Ding等（2025b）
多尺度损失函数失衡	基于神经切线核的自适应权重算法	时域	Ding等（2023b）
“软约束”边界条件失效	镜像法	时域	Ding等（2023b）
“软约束”边界条件失效	初边值条件硬嵌入	时域	Moseley等（2023）；Alkhadhr等（2023）
激活函数	自适应正弦激活函数	频域	Song等（2022）；Wu等（2024）；Chai等（2024b）
激活函数	Swish激活函数	时域	Sethi等（2023）；Ding等（2025b）
谱偏差	傅里叶特征映射或位置编码	频域	Huang等（2022）；Song等（2023b）；Wu等（2024）；Chai等（2024b）
	傅里叶特征映射或位置编码	时域	Sethi等（2023）；Ding等（2025b）
	引入满足波动方程的可学习Gabor函数	频域	Alkhalifah等（2024）
人工边界条件	完美匹配层	频域	Wu等（2023，2024）；Chai等（2024b）
	声波旁轴近似边界	时域	Ding等（2025b）
	弹性波旁轴近似边界	时域	Ren等（2024b）

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表 2 机器学习和深度学习方法在GMM中的应用

Table 2. Machine learning and deep learning methods in GMM

方法	参数	研究区域	文献来源
遗传算法	M_W、R_hyp、V_S30	土耳其	Cabalar等（2009）
模拟退火	M_W、R_hyp、V_S30、F_M	NGA-West1	Alavi等（2011）
遗传编程	M_W、R_JB、V_S30、F_M	NGA-West1	Gandomi等（2011）
支持向量机	M_W、R_Clstd、V_S30、F_M	NGA-West1	Tezcan等（2012）
Lagramge	M_W、R_JB、V_S30、F_M	NGA-West1	Markič等（2013）
人工神经网络	M_W、R_JB、V_S30、F_M、F_D	KiK-net	Derras（2014）
神经-模糊推理	M_W、R_Clstd、V_S30、F_M	NGA-West1	Thomas等（2016）
模型树M5	M_W、R_Clstd、V_S30、F_M	NGA project	Kaveh等（2016）
人工神经网络	M_W、R_rup、V_S30、F_M	NGA-West2	Dhanya等（2018）
分类回归树	M_W、R_Clstd、V_S30、F_M	NGA-West1	Hamze-Ziabari等（2018）
前缀基因表达编程	M_W、R_epi、V_S30、倾角	伊朗、土耳其、亚美尼亚等	Javan-Emrooz等（2018）
多层感知机	M_W、R_rup、V_S30、F_M	NGA-West1	Akhani等（2019）
深度神经网络	M_W、R_Clstd、V_S30、倾角	NGA-West2	Derakhshani等（2019）
人工神经网络	M_W、R_hypo、V_S30	俄克拉何马、堪萨斯和得克萨斯	Khosravikia等（2019）
二阶深度神经网络	M_W、R_JB、V_S30、Z₁、Z_TOR、结构周期、F_M、区域	NGA-West2	Ji等（2021）
U-Net	M_W、R_hyp、lnR_hyp、Z_bedrock、台站经纬度、事件经纬度	KiK-net	Lilienkamp等（2022）
贝叶斯神经网络	M_W、R_rup、V_S30、F_M、区域、F_D	NGA-West2和NGA-Sub	Sreenath等（2023b）
极端梯度提升和深度神经网络	M_JMA、R_hyp、F_D、V_S30、F_M、场地高程	KiK-net和K-Net	Dang等（2024）
深度神经网络	M_W、F_D、F_M、R_JB、V_S30	北美中部和东部	Meenak等（2025）
多方法混合	M_W、R_JB、V_S30、F_M、区域	NGA-West2	Ding等（2025a）
注：M_W为矩震级。R_hyp为震源距；R_Clstd和R_rup是断层距；R_JB是断层投影距；R_epi是震中距；V_S30是地表到地下30 m间的平均剪切波速；Z₁（m）为盆地深度；Z_TOR为到断裂顶部的深度；Z_bedrock是地表到基岩的深度；F_M是断层类型；F_D是震源深度。

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表 3 人工智能生成模型在地震动模拟中的应用

Table 3. Artificial intelligence generative models in ground motion simulation

模型类型	条件参数	数据集	文献来源
GAN	None	模拟数据集	（Matinfar等，2023）
CGAN	是否存在地震事件	俄克拉何马州3个站点地震数据	（Wang等，2021c）
	M_W、R_hyp、V_s30	K-NET, KiK-net	（Florez等，2022）
	R_epi	KiK-net	（Li等，2024b）
	M_W、R_rup、V_s30、F	混合数据集（NGA-West2, 随机有限断层法模拟数据集）	（Huang等，2024b）
	PGA强度等级标签	TSMIP	（Huang等，2024a）
	M_W、R_rup、V_s30、F	KiK-net	（Shi等，2024）
	低频地震波形和低频反应谱	K-NET	（Aquib等，2024）
	反应谱	KiK-net	（Kim等，2024）
	M_W、R_hyp、V_s30、F	KiK-net, NGA-West2	.（Masoudifar等，2025）
	M_W、R_rup、V_s30、F、滑动机制	KiK-net	（陈苏等，2026）
	PGA、M_W、R_rup、V_s5、V_s10、V_s20、Z_1.0、Z_1.4	K-NET	（Matsumoto等，2024）
StyleGAN	None	KiK-net, NGA-West2	（Xu等，2024）
VAE	None	NGA West2	（Ning等，2024）
VAE	M_W,震源坐标、台站坐标	旧金山地区1990—2022年小震数据集	（Ren等，2024a）
扩散模型	M_W,震源深度、震源坐标、台站坐标	伯克利盖塞斯与北加州的地震数据集	（Bi等，2025）
	R_hyp、M_W、V_s30	K-NET,KiK-net	（Bosisio，2024）
	震源深度、震源坐标、台站坐标、R_hyp、M_W、方位角	欧洲、北美、东亚地震数据	（Jung等，2025）
	M_W、R_rup、V_s30、F	NGA-West2	（Huang等，2025）
注：None是未输入参数；V_s5、V_s10、V_s20是地表到地下5、10、20 m平均剪切波速；Z_1.0、Z_1.4是地表到剪切波速为1.0、1.4 km/s位置的深度。

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