基于多普勒雷达的突发性灾害体智能识别

doi:10.3799/dqkx.2025.243

基于多普勒雷达的突发性灾害体智能识别

doi: 10.3799/dqkx.2025.243

乐千桤^1,2,
张瑞^1,2,
陈辉^3,1,2,
闫帅星³,
王东坡³

1. 地质灾害防治与地质环境保护全国重点实验室, 成都 610059;
2. 数学地质四川省重点实验室, 成都 610059;
3. 成都理工大学数学科学学院, 成都 610059

基金项目:

国家自然科学基金委员会(42207232)

四川省科学教育联合基金(项目编号:2024NSFSC1952)

成都市科学技术局技术创新研发项目(2024 YF05-01584-SN)

地质灾害防治与地质环境保护全国重点实验室自主研究课题(SKLGP2022Z011)

详细信息

作者简介:
乐千桤(1976-),副教授,硕士生导师,主要从事人工智能技术在地质灾害监测的研究。E-mail:leqianqi@cdut.edu.cn,ORCID:0009-00088392-5055

中图分类号: P694
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- 被引次数: 0
出版历程
- 收稿日期: 2025-07-03
- 网络出版日期: 2025-12-03

摘要

摘要: 针对突发性地质灾害识别的高精度与实时性需求，本研究提出了一种适用于多普勒雷达的轻量级多尺度特征融合网络DRWAF-Net（Doppler Radar Wavelet Attention Fuse Network），通过小波变换与注意力机制协同优化，实现了复杂地表环境下泥石流、滚石等灾害体的实时识别。研究充分利用多普勒雷达动态捕获灾害体距离与速度的能力，整合环境干扰下的泥石流数据集与RDRD数据集的核心要素，针对性构建了突发地质灾害场景的多普勒雷达数据集。实验结果表明，DRWAF-Net以2.38M参数量、9.27MB模型大小和6.31ms推理速度，使准确率（96.77%）、精确率（96.90%）、召回率（96.77%）和F1分数（96.77%）均在测试集上达到最优水平。消融实验验证，结合多输入注意力门控（MIAG）机制的DRWAF-Net较基准模型提升识别率1.87%-3.13%。本研究通过轻量化设计与实时推理优化，为突发性地质灾害应急响应提供了实时、智能的监测方案。
- 突发性地质灾害 /
- 深度学习 /
- 多普勒雷达 /
- 目标识别 /
- 注意力机制

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