城市道路塌陷已造成严重的经济损失和生命安全损害,需要精细化的探测方法查明潜在的隐患。文中提出城市道路拖曳式地震数据精细化成像方法,充分利用逆散射波、面波和反射波实现城市道路目标体的精细化成像,并建立完整的数据处理流程。依据模型数据和实测数据结果总结得出:逆散射波的横向分辨率高,能实现空洞等异常体的准确横向定位;不参与反演计算的面波快速成像方法避免反演计算的多解性和假异常,能提高横向和纵向分辨率;从反射波得到的叠加剖面能追踪反射波和绕射波的变化特征,准确分析物性分界面的横向变化特征,实现绕射点的准确定位。综合以上三种成像方法,能精细化分析空洞的横向位置和深度信息。随着拖曳式地震设备的持续发展,逆散射波、面波和反射波的数据处理方法在城市道路精细化探测工作得到广泛应用。

Urban road collapses have caused serious economic loss and life security, refined detection methods are needed to identify potential hazards. The focus of this study is to investigate the characteristics of the seismic wavefield and the data processing method specifically for urban roads. Accurate imaging is achieved through the utilization of back-scattered waves surface waves, and reflected waves. A complete data processing procedure is established. The proposed data processing methods are applied to measured data to verify the reliability of the data processing methods. According to the results of model data and measured data, the following points can be summarized: The back-scattered wavefield has high transverse resolution and can achieve accurate transverse positioning of anomalous bodies such as voids, pipelines; The surface wave fast imaging method without inversion calculation can avoid multiple solutions and false anomalies, and improve transverse and longitudinal resolution; The stack section derived from the reflected wave enables the tracking of variation patterns in both the reflected and diffracted waves. This analysis allows for the assessment of lateral variation characteristics of physical interfaces and facilitates the identification of diffraction points. When integrated with the aforementioned three imaging methods, it becomes possible to analyze the relative changes in road asphalt layer, the thickness of backfill soil layer, as well as the transverse position and depth of cavities and pipelines. As towed seismic equipment continues to advance, the data processing techniques for back-scattered wave, surface wave, and reflected wave are increasingly utilized as effective detection methods for urban roads.

TN959

近地表探测科学技术实验室基金资助项目(TCG2022B007)