| 21 Jul 2026
Selection of onset of acceleration points and failure time prediction of landslides based on ground-based radar
Abstract. The inverse velocity method (INV) based on the onset of acceleration (OOA) point is widely used in landslide time prediction. However, the selection of OOA point affects the accuracy of INV prediction results. This study proposed a deformation standard deviation-OOA (DSD-OOA) point identification method based on the statistical characteristics of ground-based radar landslide area deformation data. By introducing a controllable variable, a modified INV method was derived. The OOA point identified by DSD-OOA was substituted into the modified INV method for landslide time prediction and compared with the prediction results of the moving average-OOA (MA-OOA) point method. Results show that compared to MA-OOA, the inverse velocity time series after the OOA point identified by DSD-OOA exhibits smaller fluctuations and is closer to linear change. The INV predictions using MA-OOA (MA-OOA-INV) consistently lag behind the actual landslide time, while the INV predictions using DSD-OOA (DSD-OOA-INV) are more stable and consistently precede the actual landslide time of failure. Furthermore, the root mean square error (RMSE) and coefficient of determination (R²) indicate that the DSD-OOA-INV method predicts landslide lifetime with higher accuracy, suggesting that OOA points identified by the DSD-OOA method can more precisely predict landslide time of failure.
Reviewer Comments on egusphere-2026-4006
I have read this manuscript twice, on July 20 and July 30, 2026.
The selection of the onset of acceleration (OOA) point is a critical issue in landslide time prediction. This study proposes to identify OOA points using the standard deviation of displacement (DSD) values of pixels within the deformation zone. I acknowledge that this approach has certain practical value. However, in my assessment, this represents only a minor technical improvement and does not meet the standards of innovation required for publication in NHESS. After two careful readings, I am inclined to recommend rejection, or alternatively, major revision and resubmission. The manuscript suffers from several significant issues, which I outline below:
1. Insufficient comparison with state-of-the-art OOA identification methods
In recent years, numerous advanced methods for automatic OOA identification have been developed, including approaches based on BIC (Bayesian Information Criterion) change point detection, CUSUM (cumulative sum) algorithms, and Kalman filtering. The authors' comparison is limited to the MA-OOA-INV method, which is now more than ten years old (Carla et al., 2017). This is far from sufficient to demonstrate the superiority of the proposed DSD-OOA method. The authors must include comparisons with contemporary methods. The following recent contributions are particularly relevant:
Urgilez Vinueza et al. (2021): A new methodology to detect changes in displacement rates of slow-moving landslides using InSAR time series (EGU General Assembly)
A new data-driven approach for dynamic landslide life expectancy prediction based on kinematic features (Acta Geotechnica, 2025)
Acceleration stage detection and dynamic model selection for real-time landslide time-of-failure predictions (using Bayesian theory)
Wang, J.Z. et al. (2023): A framework for identifying the onset of landslide acceleration based on the exponential moving average (EMA) (Journal of Mountain Science)
2. The OOA identification procedure remains overly simplistic and subjective
The method for determining when DSD reaches "greater fluctuation" (Line 133) lacks a quantitative threshold. This is a critical weakness. For example, in Figure 4, the authors fit the DSD–time curve using two simple straight lines. This approach is not rigorously justified and remains semi-quantitative, as it is subject to subjective choices in the placement of the fitting segments. In fact, if one zooms in on different portions of the curve, the linear fitting equations—and consequently the identified OOA point—could shift. I suggest that the authors consider more robust techniques, such as morphological analysis or other image/curve processing methods, to address this issue objectively.
3. Case studies lack diversity and do not demonstrate general applicability
All three case studies are from open-pit mines in northwestern China, with similar lithologies and triggering mechanisms (construction vibrations). This raises a serious concern: can the proposed DSD-OOA method be applied to other types of landslides, such as slow-moving creeping landslides or rainfall-induced landslides? The authors must include a dedicated discussion on the applicability boundaries and limitations of their method, clearly specifying the conditions under which DSD-OOA is expected to perform well and where it may fail.
4. The complexity of landslide deformation is not adequately addressed
Landslide deformation is inherently complex. Many slow-moving creeping landslides exhibit step-like displacement curves, where a single episode of rapid deformation does not necessarily indicate imminent failure. In such cases, the OOA is not a fixed value. I suggest that the authors consider this as a direction for future research—for example, by employing machine learning techniques to predict OOA points probabilistically rather than deterministically, thereby accounting for the uncertainty and variability inherent in landslide deformation processes.
Overall Recommendation
In summary, while the DSD-OOA concept has some intuitive appeal and practical potential, the current manuscript does not present a sufficiently rigorous, well-validated, or broadly applicable method. The lack of comparison with state-of-the-art methods, the subjective nature of the OOA identification, the limited case study diversity, and the oversimplified treatment of complex deformation behavior collectively undermine the contribution. I recommend rejection, or at minimum, major revision with a clear plan to address the above concerns before any resubmission.