数据与计算发展前沿 ›› 2026, Vol. 8 ›› Issue (2): 66-81.

CSTR: 32002.14.jfdc.CN10-1649/TP.2026.02.006

doi: 10.11871/jfdc.issn.2096-742X.2026.02.006

• 专刊：冰冻圈大数据挖掘分析关键技术及应用 • 上一篇    下一篇

面向数据驱动建模的冰川跃动可探测特征分析

胡珂1,2,3(),孙晓兵2,3,崔文煜2,3,*(),张耀南4,5,艾鸣浩4,5,王丽莉4,5,周增光6,韩立钦7   

  1. 1 中国科学技术大学， 环境科学与光电技术学院， 安徽 合肥 230026
    2 中国科学院合肥物质科学研究院， 安徽光学精密机械研究所， 安徽 合肥 230031
    3 中国科学院通用光学定标与表征技术重点实验室， 安徽 合肥 230031
    4 中国科学院西北生态环境资源研究院， 甘肃 兰州 730000
    5 国家冰川冻土沙漠科学数据中心， 甘肃 兰州 730000
    6 中国科学院空天信息创新研究院， 北京 100094
    7 河南师范大学， 河南 新乡 453007
  • 收稿日期:2025-11-05 出版日期:2026-04-20 发布日期:2026-04-23
  • 通讯作者: *崔文煜（E-mail: cuiwenyu@aiofm.ac.cn）
  • 作者简介:胡珂，硕士研究生，中国科学技术大学环境科学与光电技术学院，主要从事冰冻圈遥感与大数据挖掘。
    本文中承担的工作为特征分析与内容撰写。
    HU Ke is currently pursuing the M.S. degree at the School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China. Her research interests include cryosphere remote sensing and big-data mining.
    In this paper, she is mainly responsible for feature analysis and manuscript writing.
    E-mail: huke-ustc@mail.ustc.edu.cn|崔文煜，博士，副研究员，中国科学院合肥物质科学研究院，中国科学院通用光学定标与表征技术重点实验室。主要从事光学信号表征、遥感机理与探测技术、光学与红外成像仿真等方面的研究。
    本文负责框架构建、内容审核与论文修改。
    CUI Wenyu is a Ph.D. is an Associate Researcher with the Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences. He is also with the Key Laboratory of Optical Calibration and Characterization, Chinese Academy of Sciences. His research interests include optical signal characterization, remote sensing mechanisms and detection technologies, and optical and infrared imaging simulation.
    In this paper, he is mainly responsible for developing the study framework, reviewing the content, and revising the manuscript.
    E-mail: cuiwenyu@aiofm.ac.cn
  • 基金资助:
    国家重点研发计划(2022YFF0711703);天水市科技计划(2022-FZJHK-3409)

Analysis of Detectable Features for Data-Driven Glacier-Surge Modeling

HU Ke1,2,3(),SUN Xiaobing2,3,CUI Wenyu2,3,*(),ZHANG Yaonan4,5,AI Minghao4,5,WANG Lili4,5,ZHOU Zengguang6,HAN Liqin7   

  1. 1 School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, Anhui 230026, China
    2 Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China
    3 Key Laboratory of Optical Calibration and Characterization, Chinese Academy of Sciences, Hefei, Anhui 230031, China
    4 Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China
    5 National Cryosphere Desert Data Center, Lanzhou, Gansu 730000, China
    6 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
    7 Henan Normal University, Xinxiang, Henan 453007, China
  • Received:2025-11-05 Online:2026-04-20 Published:2026-04-23

RichHTML

0

PDF

9

摘要：

【目的】 冰川跃动是在数月至数年尺度内发生的突发性高速前进与质量再分配的现象，是高寒地区链生自然灾害的起始诱因之一，同时也是全球气候研究的重点。为了探索冰川跃动事件的可预测性，建立基于时空连续观测数据驱动的冰川演变智能化模型是具有应用前景的解决方案。但是尚缺乏与跃动冰川内在演化进程相关度较高的可探测特征来支撑模型的数据驱动。【方法】 文中聚焦于数据驱动范式中的特征与观测层，从跃动冰川的表观变化、探测方法、数据表现、模型参数等方面，系统梳理分析了冰川跃动多源数据要素特征的可探测性、机理关联与适用边界，建立了多源可测特征体系。【结果】 分析结果表明，现有主流特征多应用于冰川跃动的事中、事后监测，缺乏对跃动演化过程和触发临界状态的表征能力。针对这一不足，文中根据从电磁信号层面挖掘与跃动触发机制具有相关性特征的思路，提出冰川跃动光谱特征指数，并通过历史回溯性试验，验证了光谱特征指数的跃动前兆特性。【结论】 以光谱特征指数为例示范了将物理机理转化为可学习特征的方法路径。光谱维度的数据特征可与速度场、InSAR相干性与厚度变化等多源特征联合，作为数据驱动模型的关键参数化支撑。

关键词: 冰川跃动, 可测特征, 遥感探测, 数据驱动

Abstract:

[Objective] Glacier surges are abrupt episodes of rapid advance and mass redistribution on monthly-to-multi-year timescales. They are among the primary initiators of cascading natural hazards in high-mountain cold regions and a key topic in global climate research. To explore the predictability of surge events, building data-driven, intelligent models of glacier evolution based on spatiotemporally continuous observations is a promising approach. However, there is still a lack of detectable data features that are strongly correlated with the internal evolutionary processes of surge-type glaciers to support such data-driven models. [Methods] Within a data-driven framework, this study focuses on the feature and observation layers. We systematically review and analyze multi-source feature elements of glacier surges, covering apparent surface changes, observation/sensing methods, data manifestations, and model parameters to assess their detectability, mechanistic linkages, and applicability bounds, and on this basis establish a multi-source system of detectable features. [Results] Our analysis shows that most mainstream indicators are designed for mid- to post-event monitoring and lack the capacity to characterize the incubation process and critical triggering states. To address this gap, we propose a spectral surge index derived from electromagnetic-signal perspectives that are mechanistically related to surge initiation. Retrospective tests reveal clear precursor characteristics in this index. [Conclusion] As an illustrative example, the proposed spectral surge index demonstrates a practical pathway for transforming physical mechanisms into learnable features. Spectral-domain features can be integrated with velocity fields, InSAR coherence, and thickness change as key parameterized inputs to data-driven models.

Key words: glacier surges, measurable indicators, remote sensing detection, data-driven