Coseismic Landslides Research Articles

Near-Real Prediction of Earthquake-Triggered Landslides on the Southeastern Margin of the Tibetan Plateau

Earthquake-triggered landslides (ETLs) feature large quantities, extensive distributions, and enormous losses to human lives and critical infrastructures. Near-real spatial prediction of ETLs can rapidly predict the locations of coseismic landslides just after a violent earthquake and is a vital technical support for emergency response. However, near-real prediction of ETLs has always been a great challenge with relatively low accuracy. This work proposes an ensemble prediction model of EnPr by integrating machine learning tree models and a deep learning convolutional neural network. EnPr exhibits relatively strong prediction and generalization performance and achieves relatively accurate prediction of ETLs. Six great seismic events occurring from 2008 to 2022 on the southeastern margin of the Tibetan Plateau are selected to conduct ETL prediction. In a chronological order, the 2008 Ms 8.0 Wenchuan, 2010 Ms 7.1 Yushu, 2013 Ms 7.0 Lushan, and 2014 Ms 6.5 Ludian earthquakes are employed for model training and learning. The 2017 Ms 7.0 Jiuzhaigou and 2022 Ms 6.1 Lushan earthquakes are adopted for ETL prediction. The prediction accuracy merits of ACC and AUC attain 91.28% and 0.85, respectively, for the Jiuzhaigou earthquake. The values of ACC and AUC achieve 93.78% and 0.88, respectively, for the Lushan earthquake. The proposed EnPr algorithm outperforms the algorithms of XGBoost, random forest (RF), extremely randomized trees (ET), convolutional neural network (CNN), and Transformer. Moreover, this work reveals that seismic intensity, high and steep relief, pre-seismic fault tectonics, and pre-earthquake road construction have played significant roles in coseismic landslide occurrence and distribution. The EnPr model uses globally accessible open datasets and can therefore be used worldwide for new large seismic events in the future.

Co-seismic deformation and related hazards associated with the 2022 Mw 5.6 Cianjur earthquake in West Java, Indonesia: insights from combined seismological analysis, DInSAR, and geomorphological investigations

IntroductionOn November 21, 2022, a magnitude Mw 5.6 earthquake struck Cianjur Regency in the West Java Province of Indonesia. It was followed by at least 512 aftershocks that persisted from November to June 2023. This seismic event occurred in an area previously unrecognized as an active fault zone. The consequences of this earthquake in Cianjur were severe, leading to both loss of life and extensive structural damage. The substantial damage to buildings was likely a result of abrupt alterations in the local topography due to surface deformation effects.ObjectivesThis research endeavor aims to spatially determine the patterns of ground surface deformation and its relationship with local geomorphological setting due to earthquakes in Cianjur in 2022.MethodsIn this study we conduct seismological analysis of 45 seismic stations, statistical analysis of mainshock and aftershocks data, RADAR Sentinel-1 imagery and employed the DInSAR methodology. Field survey was also conducted to determine the geomorphological characteristics in the study area.ResultsThe outcomes disclosed that the deformation encompassed both subsidence and uplift. The results signify that there was subsidence deformation in the vicinity of Cianjur and its environs during the primary earthquake on November 21, 2022, with an average deformation value of approximately -5 cm. In contrast, the measured deformation during the aftershocks exhibited uplift deformation, with an average value of 10 cm. The examination of deformation patterns amid the 2022 Cianjur earthquake sequence detects elevated deformation values in the vicinity of Cugenang district, with an orientation running from northwest to southeast. The geomorphological investigation conducted indicates that the region of Cianjur encompasses a variety of landforms, such as volcanic, structural, fluvial, and denudational. These landforms exhibit distinct responses to seismic activities. Co-seismic hazards, such as landslides frequently occur as a consequence of seismic events in mountainous terrain.Main ConclusionsSpatio-temporal variation of ground deformation could arise from various causes, such as the number and distribution of aftershocks, stress redistribution, fault interactions, secondary effects, and local geological settings. The mainshocks release accumulated stress along a fault, resulting in particular types of deformation, whereas aftershocks may redistribute stress exhibiting on adjacent faults. Secondary effects triggered by aftershocks, coupled with local geological and geomorphological conditions, further contribute to the diverse patterns of ground deformation observed during seismic events. The results of the study revealed that ground deformation had the greatest impact on fluvial, volcanic, and denudational processes, resulting in notable subsidence and uplift in specific regions. The occurrence and magnitude of co-seismic landslides were triggered by both mainshock and aftershock events, which occurred on weathered geological materials. These effects were further amplified by the simultaneous presence of the rainy season.ImplicationsThe knowledge gained from this research can be applied to evaluate the impacts of earthquakes and to proactively reduce future risks.

Landslides triggered by the 2022 Ms. 6.8 Luding strike-slip earthquake: An update

In this study, we established an updated coseismic landslide inventory of the 2022 Ms. 6.8 Luding earthquake. This version contains over 16,000 hand-digitized landslides with a total landslide area of 43.2 km2. We compared the spatial distribution of landslides and topographic, seismic, and geological factors on both sides of the seismogenic fault. The results show that the coseismic landslides of the Luding earthquake were mainly distributed along both sides (SWW and NEE blocks) of the seismogenic fault, with more landslides concentrated on the west side (SWW block). Compared to the NEE block, landslides on the SWW block are more likely to occur in areas characterized by higher elevations and reliefs. Otherwise, we observe an exponential decrease in both landslide area density and number density as the distance from the seismogenic fault increases, and the proximity to the fault is positively correlated with landslide size. Finally, we explored the relationship between landslide distribution and slip patterns by examining the detailed rupture characteristics of the Luding earthquake. Our analysis indicates that density patterns of coseismic landslides closely track the distribution of fault slip. The landslide abundance area corresponds to the area with the largest slip displacement of the surface rupture. The more the rupture extends into the shallow portion of the fault plane, the more landslides occur.

Optimizing seismic hazard inputs for co-seismic landslide susceptibility mapping: a probabilistic analysis

Optimizing seismic hazard inputs for co-seismic landslide susceptibility mapping: a probabilistic analysis

Debris flow volume prediction model based on back propagation neural network optimized by improved whale optimization algorithm.

Debris flow is a sudden natural disaster in mountainous areas, which seriously threatens the lives and property of nearby residents. Therefore, it is necessary to predict the volume of debris flow accurately and reliably. However, the predictions of back propagation neural networks are unstable and inaccurate due to the limited dataset. In this study, the Cubic map optimizes the initial population position of the whale optimization algorithm. Meanwhile, the adaptive weight adjustment strategy optimizes the weight value in the shrink-wrapping mechanism of the whale optimization algorithm. Then, the improved whale optimization algorithm optimizes the final weights and thresholds in the back propagation neural network. Finally, to verify the performance of the final model, sixty debris flow gullies caused by earthquakes in Longmenshan area are selected as the research objects. Through correlation analysis, 4 main factors affecting the volume of debris flow are determined and inputted into the model for training and prediction. Four methods (support vector machine regression, XGBoost, back propagation neural network optimized by artificial bee colony algorithm, back propagation neural network optimized by grey wolf optimization algorithm) are used to compare the prediction performance and reliability. The results indicate that loose sediments from co-seismic landslides are the most important factor influencing the flow of debris flows in the earthquake area. The mean absolute percentage error, mean absolute error and R2 of the final model are 0.193, 29.197 × 104 m3 and 0.912, respectively. The final model is more accurate and stable when the dataset is insufficient and under complexity. This is attributed to the optimization of WOA by Cubic map and adaptive weight adjustment. In general, the model of this paper can provide reference for debris flow prevention and machine learning algorithms.

Directivity effect of the spatial distribution of co-seismic landslides affected by near-fault ground motions

Numerous co-seismic landslides occur in near-fault areas, and their spatial distribution seems to correlate well with the distribution of the energy and pulse of near-fault ground motions (NFGMs) in all directions. To confirm the relationship between the energy and pulse characteristics of NFGMs and the spatial distribution of co-seismic landslides, three-dimensional discontinuous deformation analysis (3D-DDA) was used to investigate the effect of NFGMs on initiating co-seismic landslides through an octagonal platform model and a generalized landslide. This model was subjected to the influence of two sets of representative three-component NFGMs. The outcomes derived from the block sliding processes revealed pronounced variations in the displacements of blocks in different spatial orientations. The greatest difference was up to 4.2 times, and the sliding blocks were more prone to large displacements in the direction with large energy and pulse. This study confirmed that the distribution characteristics of co-seismic landslides are related to the energy and pulse of NFGMs and provided evidence to illuminate the special distribution characteristics phenomenon of co-seismic landslides and theoretical support for the selection of factors for landslide hazard assessment.

Earthquake Response of the Transportation Infrastructure in the Region Affected by the Feb. 6 Türkiye Earthquakes’’ Part II-Bridges and Tunnels

ABSTRACT On February 6, 2023, two devastating earthquakes occurred in Türkiye. Fault rupture, ground motion, liquefaction and co-seismic landslides caused damage to transportation networks. Transportation was interrupted for a few days. The fault rupture was close to or crossing many roads and bridges, causing significant displacements. Only two bridges collapsed, both from secondary effects of the earthquake, one from liquefaction and one from falling rock. Considering the magnitude of two earthquakes and the proximity of many of the bridges to fault rupture, bridges performed remarkably well. This paper presents the earthquake response of the bridges and the tunnels in the region.

Seismic landslide hazard assessment using improved seismic motion parameters of the 2017 Ms 7.0 Jiuzhaigou earthquake, Tibetan Plateau

Seismic landslide hazard assessment plays a very important guiding role during urgent earthquake relief. In August 2017, an Ms 7.0 earthquake in Jiuzhaigou County, Sichuan Province, China, triggered thousands of landslides. Based on the analysis of geological settings and coseismic landslide characteristics, the Newmark model is used to complete the seismic landslide hazard assessment. Three seismic motion parameters, namely, peak ground acceleration (PGA), traditional Arias intensity (Arias_P), and improved Arias intensity (Arias_C), are adopted. A publicly published coseismic landslide catalog is used as the validation samples. The results show that the coseismic landslides are mainly distributed in the deep gullies and steep mountainous slopes on the north and south sides of the epicenter. The seismic landslide hazard accuracy based on Arias_C is the best, followed by that based on PGA and Arias_P. The spatial distribution of seismic landslide hazards based on Arias_C shows an almost standard elliptical ring and is in good agreement with that of coseismic landslides. These results fully reflect the combined influence of the epicenter and seismogenic fault on landslide development. The middle seismic landslide hazard and over are mainly located at areas with seismic intensity of VII degree and above. The Arias intensity is very suitable for rapid seismic landslide hazard assessment in emergency situations. The study results can provide scientific and technological support for rapid earthquake relief and have reference significance for future seismic landslide hazard assessment.

Dams overtopping scenarios from catastrophic landslides in mountains’ headwaters Case study in Kobe City, Japan

As a Mw 8.0 Nankai Trough Earthquake is predicted with an 80% probability of occurrence within the next 30 years, the efficiency of check dams in the mountains above Kobe City is a crucial question when considering co-seismic landslide disaster risk management. In the present contribution the author aimed to define which subsection of the Sumiyoshigawa watershed may be more prone to generate impacts in downstream Kobe City from catastrophic landslides in the headwaters. For this purpose, the present state of the check dams network was analysed from the 2018 LiDAR (Light Detection and Ranging) data, considering the progressive infilling of the structures. As a result, the capacity of the dams in 2018 dropped by about 1/10 of the original designed capacity, arguably because of the heavy rainfall events in 2014 and 2018. Despite this evolution, however, landslides > 20,000 m3 starting from only one tributary can fill all the dams and flow downstream. From this data, it is possible to prioritize dam curation programs, especially because population ageing and shrinking is reducing the manpower and the funds available to maintain the check dams.

Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning

Landslides are devastating natural disasters that generally occur on fragile slopes. Landslides are influenced by many factors, such as geology, topography, natural drainage, land cover, rainfall and earthquakes, although the underlying mechanism is too complex and very difficult to explain in detail. In this study, the susceptibility mapping of co-seismic landslides is carried out using a machine learning approach, considering six districts covering an area of 12,887 km2 in Nepal. Landslide inventory map is prepared by taking 23,164 post seismic landslide data points that occurred after the 7.8 MW 2015 Gorkha earthquake. Twelve causative factors, including distance from the rupture plane, peak ground acceleration and distance from the fault, are considered input parameters. The overall accuracy of the model is 87.2%, the area under the ROC curve is 0.94, the Kappa coefficient is 0.744 and the RMSE value is 0.358, which indicates that the performance of the model is excellent with the causative factors considered. The susceptibility thus developed shows that Sindhupalchowk district has the largest percentage of area under high and very high susceptibility classes, and the most susceptible local unit in Sindhupalchowk is the Barhabise municipality, with 19.98% and 20.34% of its area under high and very high susceptibility classes, respectively. For the analysis of building exposure to co-seismic landslide susceptibility, a building footprint map is developed and overlaid on the co-seismic landslide susceptibility map. The results show that the Sindhupalchowk and Dhading districts have the largest and smallest number of houses exposed to co-seismic landslide susceptibility. Additionally, when conducting a risk analysis based on susceptibility mapping, as well as considering socio-economic and structural vulnerability in Barhabise municipality, revealed that only 106 (1.1%) of the total 9591 households, were found to be at high risk. As this is the first study of co-seismic landslide risk study carried out in Nepal and covers a regional to the municipal level, this can be a reference for future studies in Nepal and other parts of the world and can be helpful in planning development activities for government bodies.

Distribution pattern, geometric characteristics and tectonic significance of landslides triggered by the strike-slip faulting 2022 Ms 6.8 Luding earthquake

On September 5th, 2022, an Ms 6.8 earthquake struck the Luding area of Gaze Tibetan Autonomous Prefecture, Sichuan Province, China. This earthquake with a focal depth of 16 km at 29.49°N, 102.22°E (hereinafter called the Luding earthquake) was generated by a strike-slip faulting and triggered at least 5000 landslides over an area of 5000 km2. Over 80 % of landslides are distributed in areas with a seismic intensity of IX, which mainly consist of massive shallow landslides with a few topples, rockfalls, and debris flows. Statistically, coseismic landslides are prone to occur in areas with low elevations, steep hillslope gradient, and high ground motion, and most of them are distributed in four landslide abundance areas, including the Moxi area (Sub-1), the Wandong area (Sub-2), the Wanggangping area (Sub-3) and Xingfu area (Sub-4). Among them, the landslide scale and abundance index peaks in the Sub-2 and Sub-4 areas featured by the low-elevation valley landform with large relief and steep slopes. The geometric parameters of landslides show that the majority of landslides have the height difference between 15 and 150 m and a movement distance between 50 and 250 m. As the landslide area increases, the length and height of the landslide follow a growth trend which can be described by the logarithmic relationship. Compared to other strike-slip seismic events, the Luding event has the more obvious ability to trigger coseismic landslides. The spatial distribution of coseismic landslides is controlled by the seismogenic fault, and over 85 % of landslides are roughly distributed within a range of 5 km on both sides of the seismogenic fault. Overall, the west side of the seismogenic fault has higher landslide abundance and pronounced near-surface rupture, and it thus can be inferred that the seismogenic fault dips slightly westward based on the source mechanism solution and actual landslide distribution.

Full seismic waveform analysis combined with transformer neural networks improves coseismic landslide prediction

Seismic waves can shake mountainous landscapes, triggering thousands of landslides. Regional-scale landslide models primarily rely on shaking intensity parameters obtained by simplifying ground motion time-series into peak scalar values. Such an approach neglects the contribution of ground motion phase and amplitude and their variations over space and time. Here, we address this problem by developing an explainable deep-learning model able to treat the entire wavefield and benchmark it against a model equipped with scalar intensity parameters. The experiments run on the area affected by the 2015 Mw7.8 Gorkha, Nepal earthquake reveal a 16% improvement in predictive capacity when incorporating full waveforms. This improvement is achieved mainly on gentle (~25°) hillslopes exposed to low ground shaking (~0.2 m/s). Moreover, we can largely attribute this improvement to the ground motion before and much after the peak velocity arrival. This underscores the limits of single-intensity measures and the untapped potential of full waveform information.

Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China)

On August 8, 2017, a magnitude 7.0 earthquake struck Jiuzhaigou County in Sichuan Province, triggering numerous coseismic landslides. The prompt identification of these landslides is imperative for emergency rescue efforts and post-earthquake hazard assessments. Optical satellite and unmanned aerial vehicle (UAV) images are often obstructed by cloud cover and fog following earthquakes. In contrast, polarimetric synthetic aperture radar (PolSAR), unaffected by adverse weather conditions, emerges as an indispensable tool. However, the utilization of spaceborne single-temporal SAR for mapping the inventory of coseismic landslides is infrequent and encounters constraints due to several limitations. In this study, we analyzed the amplitude feature and polarimetric decomposition of multiple ground categories in a full PolSAR image, and proposed an automated method to accurately identify coseismic landslides using a single-temporal full PolSAR image. The coseismic landslide inventory following the Jiuzhaigou Earthquake was mapped and validated using high-resolution UAV images. Detailed analysis was conducted to identify error sources leading to omissions and false positives. Additionally, we evaluated various machine learning models to compare their performance with our proposed method. Finally, we conducted a comprehensive discussion on the strengths and weaknesses of different data types (PolSAR, optical satellite, and UAV) for coseismic landslide identification. Our results indicate that the proposed PolSAR-based method achieves high accuracy in coseismic landslide inventory mapping, offering an effective solution for timely post-earthquake emergency responses in complex environments and all weather conditions in the future.

Analysis of landslide susceptibility prediction accuracy with an event-based inventory: The 6 February 2023 Turkiye earthquakes

Landslide susceptibility assessment is a complex challenge explored by various scientists, but not fully resolved. In this study, we produced the landslide susceptibility map of a large region covering 38,500 km2 area in South-East Turkiye severely affected by the 6 February 2023 Kahramanmaras Earthquakes (Mw 7.7 and Mw 7.6) using an inventory produced in previous years. We employed random forest regression with a total of nine geomorphological and environmental features and evaluated the results using the co-seismic inventory with 2611 landslides compiled here. Although high accuracy was obtained from pixel-based assessments of test data split from the learning set, the independent validation set of co-seismic landslides showed that attention needs to be paid unseen features such as rare lithological units. Given the significant damage caused by latent hazards with the Kahramanmaras earthquakes, producing reliable inventories and precise landslide susceptibility maps is crucial for risk reduction and minimizing damages.

The analysis of seismic induced progressive instability and failure mechanisms: A case study

The 2008 Wenchuan Ms8.0 earthquake triggered a catastrophic landslide, i.e., the Daguangbao (DGB) landslide (1.2 ✕ 109 m3). Its shear failure occurred within a deep-seated (average depth of 400 m) saturated carbonate bedding fault in the Paleozoic Formation. The frequent seismic events in the tectonic belt make the landslide initiation mechanism more complex. Shake table tests were conducted on the DGB landslide to understand the coupling effect of multiple earthquakes, geology and hydrology. The results showed that the multiple earthquakes weakened the friction coefficient as the Boltzmann function on the sliding surface of the landslide. The friction law of the basal surface related to seismic energy was established and incorporated into the Newmark analysis. And the seismic energy consumed in the steep scarp surface release of landslide during an earthquake is also considered. It found that the traditional Newmark model overestimates the time for earthquake-induced landslides due to ignoring the steep scarp surface release stage. It proposed that evaluating pre-earthquake induced slope damage, and critical seismic energy for triggering co-seismic landslides are important for predicting post-seismic landslides.

Source area morphometry and high depletion rate of landslides may indicate their coseismic origin

Ancient coseismic landslides indicate prehistoric earthquakes and may also be a measure of their intensity. Their identification in the landscape remains, however, challenging. We used field geologic observations of source areas of recent rainfall-induced and earthquake-induced landslides to define a morphometric index that can distinguish between these two types of triggers. Morphometries of 129 landslides from tropical, subtropical and temperate zones developed in a variety of bedrock types were described by using the following parameters: i) depletion index (Id), i.e. the ratio between the length of the depleted part and the length of the source area; ii) relative slope height (Hrel) and iii) source area angle (φS), i.e. the angle between the top of the line connecting the scarp top with the toe of the source area and the horizontal plane. They were obtained using globally available digital elevation models and satellite images. Their combination into an Index of Potential Trigger (IPT) successfully distinguished between the two types of landslide triggers with an overall accuracy of 87%. The rainfall-induced landslides had a significantly lower average IPT value of 0.01 as compared with 0.37 for the coseismic landslides. The index of potential trigger may be a useful and readily implemented morphometric tool for preliminary identification of prehistoric coseismic landslides recorded in the current topography.

Comparison of informative modelling and machine learning methods in landslide vulnerability evaluation – a case study of Wenchuan County, China

After the earthquake in Wenchuan in 2008, landslides have been happening frequently, causing a buildup of sediment in slopes and gullies. This creates a potential for mudslides and flash floods. It is important to create a model to assess the likelihood of landslides in Wenchuan County for disaster prevention. This study focuses on Wenchuan County, using 6180 co-seismic landslide sites as data. The relevant survey data is combined, and slope, normalized difference vegetation Index (NDVI), peak seismic acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance from the road, distance from the fault, distance from the water system, relief intensity, aspect, curvature, land use type, and lithologic characters are chosen. The significance of the co-seismic landslide key influence factors is discussed. The study area’s disaster susceptibility was assessed using the informative hierarchical analysis method model (I-AHP) and fully connected neural network (FCNN) model; the evaluation’s findings mostly agreed with the survey’s actual findings. The study’s findings demonstrated that: (1) the FCNN model’s AUC value was 0.910, while the I-AHP model’s AUC value was 0.768, and the ROC curve and AUC value analysis indicated that the FCNN model outperformed the I-AHP model. (2) The three most significant influencing factors are lithology, topographic relief, and distance from roads; they account for 29.5%, 11%, and 10% of the total, respectively. Thus, landslide hazard research should be done in the high terrain undulating areas surrounding the road, and rock monitoring should be intensified in post-earthquake landslide-prone areas like Wenchuan County. (3) Given that Yingxiu Town is the epicentre of the region and that landslide disasters are highly likely to occur there, technical support for landslide disaster prediction, forecasting, safe prevention, and control in the area needs to be strengthened.

Accelerating Cross-Scene Co-Seismic Landslide Detection Through Progressive Transfer Learning and Lightweight Deep Learning Strategies

Accelerating Cross-Scene Co-Seismic Landslide Detection Through Progressive Transfer Learning and Lightweight Deep Learning Strategies

Mapping Co-Seismic Landslides in Vegetated Areas by Incorporating Tri-temporal Logical Information in Change Detection Method

Mapping Co-Seismic Landslides in Vegetated Areas by Incorporating Tri-temporal Logical Information in Change Detection Method

An essential update on the inventory of landslides triggered by the Jiuzhaigou Mw6.5 earthquake in China on 8 August 2017, with their spatial distribution analyses

On August 8, 2017, a magnitude Mw6.5 (Ms7.0) earthquake occurred in Jiuzhaigou County, Aba Prefecture, in the northern part of Sichuan Province, China, with a focal depth of 20 km and an epicenter located at (33.2°N, 103.8°E). Due to the significant magnitude of the earthquake, a large number of coseismic landslides were triggered. Despite previous research conducted by experts on the landslides caused by the Jiuzhaigou earthquake, the actual number of landslides has been severely underestimated in the previously published papers. Through field surveys and visual interpretation of high-resolution remote sensing images before and after the mainshock, we have established a detailed inventory of earthquake-induced landslides. The results indicate that the event caused a minimum of 9428 landslides covering a total area of 18.82 km2. These landslides are mainly distributed in the IX intensity area of the earthquake. The landslides mainly consist of medium-sized landslides and debris flows. They predominantly occur in areas with an altitude ranging from 2600 m to 3600 m, with slopes greater than 30° and facing east and southeast. The Lower Carboniferous and Middle Carboniferous formations are more prone to triggering landslides, and landslides are more concentrated within 1 km of roads and in forested areas. Additionally, as the distance from roads and the epicenter increases, the values of LAP and LND decrease, indicating a positive correlation between the two. There are more landslides within 2 km from the fault and within a range of 6 km–9 km from the epicenter. In conclusion, this study provides a comprehensive landslide inventory with broader coverage and increased accuracy. It also conducts a comprehensive analysis of the spatial distribution patterns of landslides. This contributes to a deeper understanding of the causes of coseismic landslides and further research on the impact of landslides in affected areas.