the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Unravelling Landslide Failure Mechanisms with Seismic Signal Analysis for Enhanced Pre-Survey Understanding
Abstract. Seismic signals, with their remote and continuous monitoring advantages, have been instrumental in unveiling various landslide characteristics and have been widely applied in the past decades. However, a few studies have extended these results to provide geologists with pre-survey information, thus enhancing the understanding of the landslide process. In this research, we utilize the deep-seated Cilan Landslide (CL) as a case study and employ a series of seismic analyses, including spectrogram analysis, single force inversion, and geohazard location. These techniques enable us to determine the physical processes, sliding direction, mass amount estimation, and location of the deep-seated landslide. Through efficient discrete Fourier transform for spectrograms, we identified three distinct events, with the first being the most substantial. Further analysis of spectrograms using a semi-log frequency axis generated by discrete Stockwell transform revealed that Event 1 consisted of four sliding failures occurring within thirty seconds with decreasing sliding mass. Subsequent Events 2 and 3 were minor toppling and rockfalls, respectively. Geohazard location further constrained the source location, indicating that Events 1 and 2 likely originated from the same slope. Subsequently, the sliding direction retrieved from single force inversion and volume estimation was determined to be 153.67º and 557,118 m3, respectively, for the CL. Geological survey data with drone analysis corroborated the above seismological findings, with the sliding direction and source volume estimated to be around 148° and 664,926 m3, respectively, closely aligning with the seismic results. Furthermore, the detailed dynamic process observed in the spectrogram of Event 1 suggested a possible failure mechanism of CL involving advancing, retrogressing, enlarging, or widening. Combining the above mechanism with geomorphological features identified during field surveys, such as the imbrication-like feature in the deposits and the gravitational slope deformation, with event video, infers the failure mechanism of retrogression of the Event 1 after shear-off from the toe. Then, the widening activity was caused by the failure process for subsequent events, as Events 2 and 3. This case study underscores the significance of remote and adjacent seismic stations in offering seismological-based landslide characteristics and a time vision of the physical processes of landslides, thereby assisting geologists in landslide observation and deciphering landslide evolution.
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RC1: 'Comment on egusphere-2024-1267', Anonymous Referee #1, 30 Aug 2024
Dear Authors,I found your paper very interesting, especially from the fact that youcan use seismology data to recreate the slope movement and direction.I cannot say I have anything to add to the paper.The only one thing I would like to address is the use of the method for the landslide size and type.From the data presented in the paper, it looks like the method is completely valid for large landslides, meaning large mass activation. How would your method be applied to medium to small landslide occurences?Thank youCitation: https://doi.org/
10.5194/egusphere-2024-1267-RC1 -
RC2: 'Comment on egusphere-2024-1267', Anonymous Referee #2, 01 Oct 2024
The study presented by Chang et al. is a valuable contribution to the analysis of landslide failure using existing seismic stations, expanding the application limits of previous research. However, several aspects require further review and justification to ensure the accuracy and rigor of the results.
Although the authors have selected existing numerical models (SF) and explained them clearly, a more detailed evaluation of their suitability for the study area and previous validation under similar conditions is essential. This would involve assessing the potential biases and limitations of the models and providing a detailed justification for their selection. Additionally, incorporating a more comprehensive validation of landslides would help avoid biases and ensure result accuracy. Considering terms like "probable landslide" could also enhance clarity and precision. I tried to review the video in S1, but was not available.
The detection of individual landslide events is a critical aspect of the study. To ensure the accuracy of the results, it is crucial to verify that detected events are individual and not the result of superimposed seismic signals. The authors could provide evidence to support that only one landslide is being observed at each time step, thereby strengthening the reliability of the findings.
In Section 4.2, correlating landslide events and explicitly locating them on the area's geology and soils is vital. This would enable the assessment of the accuracy of the used density and avoid biases in mass/volume estimation. Furthermore, correlating events could facilitate the identification of patterns and trends in landslide occurrence, ultimately contributing to more effective prediction and prevention strategies. I would suggest increase the discussion using https://doi.org/10.29382/eqs-2020-0034
Although the usage of digital elevation models (DEMs) is a strength of the study, the comparison between LiDAR and Drone-derived DEMs requires careful evaluation. Considering control points can clarify potential biases in reported heights or the need for vertical bias correction. This would ensure that the DEMs accurately represent the study area's topography. I would suggest consider https://doi.org/10.1111/tgis.12819 in your assessment.
While the study provides valuable insights into landslide failure in the selected study area, it would be beneficial to discuss the potential applications of the methodology in different environmental contexts. For instance, how would the approach perform in regions with varying geological formations, climate conditions, or vegetation cover? Exploring these questions could enhance the study's relevance and generalizability, ultimately contributing to a more comprehensive understanding of landslide dynamics. The authors may consider incorporating case studies or comparative analyses to demonstrate the versatility of their approach. Please, consider discuss https://doi.org/10.5194/nhess-21-3015-2021, https://doi.org/10.1016/j.jhydrol.2021.126294, https://doi.org/10.1007/s10346-023-02179-4
Citation: https://doi.org/10.5194/egusphere-2024-1267-RC2
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