Fusion of geotechnical and geophysical data for 2D subsurface site characterization using multi-source Bayesian compressive sampling

作者：Xu, Jiabao; Wang, Yu^*; Zhang, Lulu

来源：Canadian Geotechnical Journal, 2022, 59(10): 1756-1773.

DOI：10.1139/cgj-2021-0323

• site characterization
• data fusion
• geotechnical data
• geophysical data
• Bayesian method
• compressive sensing

Subsurface site characterization is essential for geotechnical engineering applications (e.g., slope stability analysis and deep excavation design), which is usually achieved through geotechnical site investigation and might be supplemented by geo-physical survey. Geotechnical and geophysical investigations are complementary in many aspects. Geotechnical investigation provides direct measurement data with high accuracy but only at limited locations. On the other hand, geophysical survey provides abundant two-dimensional (2D) or three-dimensional measurements, but the data are often indirect. In addition, geotechnical and geophysical data are usually correlated. Therefore, fusion of geotechnical and geophysical data during site characterization is beneficial. This paper proposed a novel data fusion method, called multi-source Bayesian compressive sam-pling, for fusion of geotechnical and geophysical data and statistical characterization of 2D subsurface profiles. The proposed method is data-driven and non-parametric, without the need for an empirical parametric function between geotechnical and geophysical data. The proposed method was illustrated and validated using both numerical and real-life examples. The re-sults show that the proposed method not only properly characterizes 2D subsurface profiles but also explicitly quantifies the statistical uncertainty associated with the site characterization.

• 单位
  上海交通大学; y