Quantitative methods for landslide subsurface deformation based on acoustic emission monitoring

Slope instability early warning systems are used to monitor landslide deformation to ensure proper stakeholders make timely safety decisions and take emergency actions. Acoustic emission signals are constantly produced during landslide deformation and can be employed to monitor slope stability. Acoustic emission technology using an active waveguide has gradually become an effective monitoring method for subsurface deformation of soil landslides. It has the characteristics of low cost and high sensitivity for early detection of minor deformation within slopes. Therefore, acoustic emission technology is anticipated to increase the success rate of landslide risk early warning.

Based on large-scale landslide model experiments and field monitoring studies, the interpretation methods for acoustic emission monitoring data evolved from qualitative to quantitative. Many landslide on-site tests using acoustic emission monitoring revealed a proportional correlation between acoustic emission rate and landslide velocity. This paper described an empirical formula method for quantifying landslide subsurface deformation behavior using acoustic emission data, extracting landslide movement information, and examining the change pattern by inversely calculating landslide displacement, velocity, and acceleration. The threshold of acoustic emission parameters that trigger landslide warnings could be obtained based on the landslide velocity classification standard. However, challenges remained in developing widely applicable methods for quantifying acoustic emission data, such as the diversity of conditions in the monitoring equipment and the complexity of the interaction within the active waveguide. These challenges limited the accurate quantification of the deformation-acoustic emission response relationship, and thus, the reliability of landslide warning results could not be ensured. To overcome the above limitations, a machine learning approach was proposed, which could automatically interpret acoustic emission monitoring data and quantify the response relationship between deformation and acoustic emission. An automatic classification model for the landslide motion state and a prediction model for landslide displacement were developed to accurately measure the representative deformation characteristics, including landslide velocity, acceleration, and displacement. The classification model was trained using two acoustic emission parameters (ring down count, change rate of ring down count) and the actual labels of the landslide kinematic state. Only the two acoustic emission parameters were input to the trained classifier and kinematic labels were produced through model prediction. A machine learning-based landslide displacement prediction method was developed, where landslide displacement can be automatically measured using acoustic emission data and related parameters (e.g., rainfall). Based on the output results from machine learning classification and prediction, a method for landslide early warning with graded risk was then developed, considering the response to negative circumstances such as missing data.

Finally, this article discusses the tendency to choose acoustic emission data interpretation methods for different application scenarios and alludes to the limitations and development trends of these interpretation methods. Machine learning is the current trend in acoustic emission data analysis methods, which can increase the reliability of landslide risk warning systems. In the future, a full waveform data-based acoustic emission analysis method will be introduced for landslide deformation monitoring. It is hoped that acoustic emission technology will be developed as a universal monitoring technique for soil landslide subsurface deformation.