基于多普勒雷达的突发性灾害体智能识别

乐千桤; 张瑞; 陈辉; 闫帅星; 王东坡

doi:10.3799/dqkx.2025.243

Volume 51 Issue 4

Apr. 2026

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Le Qianqi, Zhang Rui, Chen Hui, Yan Shuaixing, Wang Dongpo, 2026. Intelligent Identification of Sudden Geohazard Bodies Based on Doppler Radar. Earth Science, 51(4): 1229-1244. doi: 10.3799/dqkx.2025.243

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Le Qianqi, Zhang Rui, Chen Hui, Yan Shuaixing, Wang Dongpo, 2026. Intelligent Identification of Sudden Geohazard Bodies Based on Doppler Radar. Earth Science, 51(4): 1229-1244. doi: 10.3799/dqkx.2025.243

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Intelligent Identification of Sudden Geohazard Bodies Based on Doppler Radar

doi: 10.3799/dqkx.2025.243

Le Qianqi^{1, 2
,
,},
Zhang Rui^{1, 2},
Chen Hui^{1, 2, 3
,
,
,},
Yan Shuaixing³,
Wang Dongpo³

1.
Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, China
2.
School of Mathematical Sciences, Chengdu University of Technology, Chengdu 610059, China
3.
State Key Laboratory of Geohazard Prevention and Geo-Environment, Chengdu University of Technology, Chengdu 610059, China

Received Date: 2025-07-03
Publish Date: 2026-04-25

Abstract

Abstract

To address the demands for high-accuracy and real-time identification of sudden geological hazards, a lightweight multi-scale feature fusion network for Doppler radar, termed DRWAF-Net (doppler radar Wavelet Attention Fuse Network), is proposed. By jointly integrating wavelet transform and attention mechanisms, the proposed method enables real-time recognition of debris flows, rockfalls, and other hazard targets under complex surface conditions. The study fully exploits the capability of Doppler radar to dynamically capture the range and velocity characteristics of moving hazard bodies, and constructs a Doppler radar dataset for sudden geological hazard scenarios by integrating key elements from a debris flow dataset under environmental interference and the RDRD dataset. Experimental results show that DRWAF-Net achieves superior performance on the test set with only 2.38 M parameters, a model size of 9.27 MB, and an inference time of 6.31 ms, attaining an accuracy of 96.77%, precision of 96.90%, recall of 96.77%, and an F1-score of 96.77%. Ablation experiments further demonstrate that the introduction of a multi-input attention gating (MIAG) mechanism improves recognition accuracy by 1.87%-3.13% compared with baseline models. Owing to its lightweight design and real-time inference capability, the proposed approach provides an effective and intelligent monitoring solution for emergency response to sudden geological hazards.
- sudden geological hazard,
- deep learning,
- Doppler radar,
- target recognition,
- attention mechanism,
- engineering geology

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References(67)

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Intelligent Identification of Sudden Geohazard Bodies Based on Doppler Radar

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