中国滑坡灾害数量多、分布广、危害大, 每年约有80%的地质灾害发生在已查明的隐患点范围之外。监测预警是主动防范地质灾害的重要手段。变形是滑坡最为关键的监测参数。该文旨在梳理总结滑坡变形监测技术和预警方法, 评述现有研究进展, 分析未来发展趋势, 为滑坡灾害防控领域的科学研究和工程实践提供借鉴。地表变形监测技术多易受到外界因素干扰, 及时性和可靠性难以得到保障。深部变形监测技术直接获取滑面发展破坏信息, 能够发现灾变前兆, 但现有设备存在成本高、量程小或操作难等问题。滑坡监测为早期预警提供依据, 而早期预警能够减少甚至避免人员伤亡和财产损失, 区域性气象预警和单体滑坡预警两类预警方法正在逐渐发展完善中。单体滑坡预警常见方法是分析滑坡变形趋势和突变特征, 通过速度、加速度等判据的阈值触发不同级别预警。滑坡智能预警模型将机器学习与地质工程交叉融合, 有望提升灾害早期预警的准确性和自动化水平。滑坡高质量监测应综合运用多种技术以克服单一技术的局限, 而预警模型则越来越关注变形趋势的智能分析。滑坡动力学机制复杂、个性特征明显, 广泛适用的预警模型有待进一步探索。

Significance: Landslide hazards are widely distributed in China and are severely harmful. The registered landslide hazards have achieved remarkable benefits in disaster reduction through a comprehensive prevention and control system. However, approximately 80% of all geo-disasters in China still occur outside the scope of identified hazards yearly. Therefore, monitoring and early warning are important means to actively prevent landslide disasters and achieve great success in disaster mitigation owing to promptness, effectiveness, and relatively low-cost advantages. Deformation is the most significant monitoring parameter for landslides and has become a focus and general trend. Landslide deformation monitoring engineering has strict requirements for controlled cost and high reliability to achieve widespread application and accurate early warning. Therefore, the commonly used monitoring instruments focus on surface deformation and rainfall to meet the requirements for easy equipment installation and low implementation cost. However, surface deformation and rainfall are not sufficient conditions to determine the occurrence of landslides. Various challenges exist in the existing monitoring technologies and early warning methods regarding engineering feasibility and performance improvement. Thus, it is important and urgent to summarize the existing research to rationally guide future development. Progress: The deformation monitoring methods are divided into surface and subsurface monitoring. Most surface deformation monitoring technologies are vulnerable to the interference of terrain, environment, and other factors; therefore, their timeliness and reliability are not easily guaranteed. Additionally, slope subsurface deformation monitoring technologies can directly obtain the development and damage information of the sliding surface; thus, they can recognize the disaster precursor. Subsurface monitoring has advanced early warning ability; however, the existing instruments have problems, such as high cost, small measuring range, or difficult operation. Acoustic emission technology has the advantages of low cost, high sensitivity, and continuous real-time monitoring of large deformation, and has gradually developed into an optional method for landslide subsurface deformation monitoring. Thus, efficient landslide monitoring should comprehensively use multiple technologies to overcome the limitations of a single technology, and an integrated monitoring system becomes the state-of-the-art trend. The purpose of landslide monitoring is to provide a basis for decision-making of disaster early warning, thus, avoiding casualties and property losses through effective early warning efforts. In the field of early warning, regional meteorological and individual landslide early warning methods are gradually developed and improved. Deformation monitoring data are the main basis for landslide early warning, and experts analyze the deformation trend and sudden change characteristics. Different early warning levels could be triggered by the threshold values of velocity, acceleration, or other criteria. However, a landslide has complex dynamic mechanisms and individual differences; thus, the generic early warning model needs further exploration. The intelligent early warning model integrates machine learning technology with geological engineering analysis to improve the accuracy and automation level of landslide early warning. Conclusions and Prospects: Deformation monitoring is essential in landslide prevention, and deformation data are the main basis for landslide early warning. Moreover, surface monitoring technologies have been widely used in the perception and decision-making process of landslides. Subsurface monitoring technologies can detect early precursors of landslide evolution to continuously improve early warning accuracy. Analyses show that early warning methods can be improved in the future by integrating machine learning models and geotechnical engineering.