测绘通报 ›› 2025, Vol. 0 ›› Issue (7): 169-173.doi: 10.13474/j.cnki.11-2246.2025.0728

• 技术交流 • 上一篇    下一篇

多传感器数据融合的边坡滑坡预警模型与应用

王贻朋1, 徐大伟2, 魏明阳1, 李波1, 胡慧敏1, 杨明生1, 徐玉玲1   

  1. 1. 中石化石油工程地球物理有限公司北斗运营服务中心, 江苏 南京 211100;
    2. 东南大学交通学院, 江苏 南京 211189
  • 收稿日期:2024-12-16 发布日期:2025-08-02
  • 作者简介:王贻朋(1983—),男,硕士,高级工程师,研究方向为北斗高精度定位应用。E-mail:sl-wangyp.osgc@sinopec.com

Landslide early warning model and application based on multi-sensor data fusion

WANG Yipeng1, XU Dawei2, WEI Mingyang1, LI Bo1, HU Huimin1, YANG Mingsheng1, XU Yuling1   

  1. 1. BeiDou Operation Service Center of Sinopec Petroleum Engineering Geophysics Co., Ltd., Nanjing 211100, China;
    2. School of Transportation, Southeast University, Nanjing 211189, China
  • Received:2024-12-16 Published:2025-08-02

摘要: 边坡滑坡作为一种突发性、高破坏性的地质灾害,严重威胁着人类社会的生产生活安全。由于单一传感器对多因素耦合效应的识别能力不足,使得滑坡预警的全面性和准确性受到限制,因此本文提出了一种基于BP神经网络的多传感器融合预警模型。借助于BP神经网络非线性特征提取能力,分别对倾斜仪、GNSS位移传感器和雨量传感器的数据进行合理训练与预测,综合3种传感器归一化的预测数据,利用加权评分的方式融合多传感器预测结果,完成滑坡风险的最终评分,建设高效、准确的监测系统。该预警系统在某输油管道附近的边坡被成功应用并取得较好效果,具有较高的推广价值。

关键词: 滑坡监测, 多传感器数据融合, BP神经网络, 风险评分, 地灾检测

Abstract: Landslides,as a sudden and highly destructive geological hazard,pose severe threats to the safety of human production and livelihoods.The limited capability of single sensors to recognize multi-factor coupling effects hinders the comprehensiveness and accuracy of landslide early warning systems.To address this limitation,this paper proposes a multi-sensor fusion early warning model based on the BP neural network.Leveraging the nonlinear feature extraction capabilities of the BP neural network,the data from inclinometers,GNSS displacement sensors,and rainfall sensors are trained and predicted individually.The normalized predictions from these three sensors are then integrated using a weighted scoring method to achieve the final landslide risk assessment,forming an efficient and accurate monitoring system.The proposed early warning system has been successfully applied to a specific slope near the a certain oil pipeline,demonstrating promising results and significant potential for broader applications.

Key words: landslide monitoring, multi-sensor data fusion, BP neural network, risk scoring, geological hazard detection

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