Unraveling 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 transforms 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 30 s 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 the volume estimation were determined to be 153.67° 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. By 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 video from the event, we can infer the failure mechanism of retrogression of Event 1 after shear-off from the toe. Then, the widening activity was caused by the failure process for subsequent events, like 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.

Authors

Institutions

Publication Details

Journal
Natural hazards and earth system sciences
Published
2025-02-03
DOI
https://doi.org/10.5194/nhess-25-451-2025
Citations
3
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
7.27

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Unraveling landslide failure mechanisms with seismic signal analysis for enhanced pre-survey understanding

Wei‐An Chao, Tung-Chou Hsieh, Ming-Wan Huang, Chin‐Shang Ku et al.
3 citations
Natural hazards and earth system sciences
Landslides and related hazards
7.27
article

Unraveling landslide failure mechanisms with seismic signal analysis for enhanced pre-survey understanding

Wei‐An Chao, Tung-Chou Hsieh, Ming-Wan Huang, Chin‐Shang Ku, Jui‐Ming Chang, Chi-Yao Hung, Che‐Ming Yang
article en
3 citations

Abstract

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 transforms 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 30 s 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 the volume estimation were determined to be 153.67° 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. By 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 video from the event, we can infer the failure mechanism of retrogression of Event 1 after shear-off from the toe. Then, the widening activity was caused by the failure process for subsequent events, like 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.

Natural hazards and earth system sciencesVol. 25(2)
National United University (TW), National Yang Ming Chiao Tung University (TW), National Chung Hsing University (TW), Yung Shin Pharmaceutical Industrial (Taiwan) (TW), Institute of Earth Sciences, Academia Sinica (TW)
National Science and Technology Council
Openalex Percentile: Top 4%
Landslides and related hazards
7.27
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.