Research on the application of integrated investigation method in rocky slope sliding surface identification

This paper reviews integrated methods for identifying sliding surfaces in rock slopes, essential for slope stability analysis. Traditional geological surveys often fail in complex conditions, while combining geological surveys, geophysics, remote sensing, and AI-based analysis enhances accuracy. Methods such as seismic refraction, GPR, LiDAR, and machine learning are discussed. A case study demonstrates improved precision using these integrated approaches. AI models, when combined with geological data, significantly enhance identification. Challenges like data quality and complexity are noted, and future research directions are suggested to improve these methods. This review highlights the potential of multi-technology integration for advancing slope stability analysis.I.