Coseismic Landslides Research Articles

The relationship between three-dimensional coseismic displacement and distribution of coseismic landslides

The relationship between three-dimensional coseismic displacement and distribution of coseismic landslides

Spatial and temporal evolution of long-term debris flow activity and the dynamic influence of condition factors in the Wenchuan earthquake-affected area, Sichuan, China

In recent decades, there have been several extreme earthquakes around the world, with the 2008 Wenchuan earthquake resulting in the highest number of landslides and substantial deposition of loose material on steep terrain and deep gullies, thereby promoting post-earthquake debris flows. Our monitoring indicates that debris flows have become long-term threats for over a decade after the earthquake, and it is crucial to determine the long-term spatial and temporal changes of debris flows and the evolving influence of condition factors in the earthquake-affected area. Thus, we established a database of 1628 debris flow events occurring between 2000 and 2020 within the 31,627 km2 area affected by the Wenchuan earthquake. The database included information on the date of occurrence, GNSS (Global Navigation Satellite System) coordinate at the gully outlet, event washout volume, and scale level obtained from various sources. Post-seismic debris flow activity witnessed a significant increase, followed by oscillation with rainfall and gradual weakening. Using the average washout volume for each scale in different periods, we applied a grid calculation method to characterize the debris flow intensity (DFI) using the total debris flow volume of cells in different epochs to study the spatial and temporal evolution of debris flow activity. Thus, based on the DFI calculation, we introduced the certainty factor and Geodetector to determine the dynamic evolution of condition factors. Our results indicate that lithology, rainfall, and slope degree primarily controlled debris flows before the earthquake. However, coseismic landslides became the main promoting factor after the earthquake. As loose materials were consumed, hydrodynamic and topographic factors began to play a more significant role in controlling debris flows. During this process, the requirements for rainfall, slope degree, and terrain relief amplitude increased over time. Based on the annual volume of debris flows from 2000 to 2020, we developed a preliminary conceptual model, which indicated that post-earthquake debris flow activity can be categorized into three phases: active (2008–2013), unstable (2014–2033), and recession (2034–2045), with the debris flow activity anticipated to return to pre-earthquake levels by 2045.

Comparison of empirically-based and physically-based analyses of coseismic landslides: A case study of the 2016 Kumamoto earthquake

This study compares the performance of empirically- and physically-based methods for predicting coseismic landslides around Aso caldera during the 2016 Mw 7.0 Kumamoto earthquake. The physically-based method couples a regional-scale wave propagation simulation with a site-scale Newmark-type sliding analysis, while the empirically-based method predicts sliding displacements based on seismic recordings and/or ground motion prediction equations. By using the well-documented landslide inventory, the predictive capacity of each method is quantitatively assessed.The case study demonstrates that the physically-based method has the advantage of simulating complicated near-fault and topographic effects. It captures around 44% of observed landslides at the caldera rim, which is significantly better than the 20% captured from the empirically-based prediction. Both methods have similar prediction performance, capturing around 56% actual landslides at the central cone region. The empirically-based method is simple to use and provides fast landslide prediction. It can be further improved by incorporating a simple prediction model for the topographic amplification effect, which increases the percentage of captured landslides by around 8% across the caldera rim.

A Heterogeneous Sampling Strategy to Model Earthquake-Triggered Landslides

Regional modeling of landslide hazards is an essential tool for the assessment and management of risk in mountain environments. Previous studies that have focused on modeling earthquake-triggered landslides report high prediction accuracies. However, it is common to use a validation strategy with an equal number of landslide and non-landslide samples, scattered homogeneously across the study area. Consequently, there are overestimations in the epicenter area, and the spatial pattern of modeled locations does not agree well with real events. In order to improve landslide hazard mapping, we proposed a spatially heterogeneous non-landslide sampling strategy by considering local ratios of landslide to non-landslide area. Coseismic landslides triggered by the 2008 Wenchuan Earthquake on the eastern Tibetan Plateau were used as an example. To assess the performance of the new strategy, we trained two random forest models that shared the same hyperparameters. The first was trained using samples from the new heterogeneous strategy, and the second used the traditional approach. In each case the spatial match between modeled and measured (interpreted) landslides was examined by scatterplot, with a 2 km-by-2 km fishnet. Although the traditional approach achieved higher AUCROC (0.95) accuracy than the proposed one (0.85), the coefficient of determination (R2) for the new strategy (0.88) was much higher than for the traditional strategy (0.55). Our results indicate that the proposed strategy outperforms the traditional one when comparing against landslide inventory data. Our work demonstrates that higher prediction accuracies in landslide hazard modeling may be deceptive, and validation of the modeled spatial pattern should be prioritized. The proposed method may also be used to improve the mapping of precipitation-induced landslides. Application of the proposed strategy could benefit precise assessment of landslide risks in mountain environments.

Earthquake-induced landslides coupled to fluvial incision in Andean Patagonia: Inferring their effects on landscape at geological time scales

Earthquakes can deeply shape the mountainous landscape through co-seismic landslides, generating large amounts of sediment that are then transported and distributed by rivers, controlling the landscape evolution. This influence is observed in the Liquiñe-Ofqui Fault System (LOFS), an active intra-arc fault system extending hundreds of kilometers through the Andes in Chilean Patagonia. For example, on April 21, 2007, a 6.2 Mw earthquake in the Aysén Fjord triggered over 500 landslides with volumes reaching 12–20 Mm3. Although there is a well-defined recurrence time, no study has focused on the effects of co-seismic landslides and sedimentary dynamics on the evolution of this mountainous landscape. In this research, we seek to improve the long-term understanding of the interaction between landslides and fluvial incisions in this segment of the Andes. For this reason, we implemented the HyLands landscape evolution model to simulate landslide activity coupled to fluvial incision. We consider the landslides that occurred during the 2007 earthquake as a precedent and simulate nine scenarios of ten seismic cycles over 21,000 years based on the 2100-year recurrence time documented in this region for the Holocene. We further used multiple uplift rates, sediment erodibility, drainage area, and channel slope exponent ratios (m/n) associated with the stream power law to assess the parameterization's impact on the landscape. According to our results, earthquake-induced landslides are a fundamental mechanism in the landscape's evolution in this region. Deposits from landslides can create transitory landscape forms that can intervene in fluvial dynamics. A significant part of the landslide sediment can remain on the slopes for thousands of years. We identified that parameterization considerably impacts the evolutionary response of the landscape in the evaluated time scale. Low m/n ratios can generate a different evolutionary response than other scenarios because the slopes are constantly driven towards their threshold angle, intensifying the interaction between landslides and fluvial incisions. Based on our analysis and considering the seismic history of the Aysén Fjord, we can explain a critical primary control of the LOFS on landscape erosion and sediment production. Implementing a hybrid landscape evolution model can help to infer the contribution of sediments associated with large earthquakes and improve the understanding of the role of landslides in the evolutionary history of Andean Patagonia. However, we stress that it is essential to advance in capturing erodibility and incision parameters of the stream power law in the Andes and local geomechanical information. Finally, we believe the landscape evolution models can help to deepen the knowledge of these processes in other Andean basins exposed to these geomorphic processes.

The age and potential causes of the giant Green Lake Landslide, Fiordland, New Zealand

Landslide deposits preserved in the geological record afford opportunities to better inform hillslope and seismic hazard and risk models, particularly in regions where observational records are short. In the Southern Alps of New Zealand, small coseismic landslides are frequent, but the geological record preserves several instances of more substantial (> 1 km3) but infrequent mass failures. With an estimated volume of 27 km3, the giant Green Lake Landslide represents one of the largest subaerial landslides on Earth. Previous work has suggested this deep-seated mass movement was most likely triggered by high-intensity seismic shaking, but that local structural weakness and/or glacial debuttressing may help to explain the anomalously large failure volume. Resolving the potential contribution of the latter is important given predictions concerning alpine deglaciation in the coming decades to centuries. Key to resolution are secure chronologies of landslide emplacement and past glacier change. Here we present in situ cosmogenic 10Be exposure ages from the Green Lake Landslide that suggest an emplacement age of 15.5 ± 0.7 ka. Recent work shows that glacial retreat in the region was underway by 19 ka, indicating that the Green Lake Landslide was emplaced 3–4 kyr after the onset of glacier retreat. Given the geometry of the former confining valley glacier, we expect that the deglaciation-landslide age gap is closer to the upper end of this estimate. If correct, this conclusion places greater weight on the roles of local geological structure and/or a great earthquake as factors contributing to the exceptionally large volume of this event.

Modeling the area of co-seismic landslides via data-driven models: The Kaikōura example

The last three decades have witnessed substantial developments in data-driven models for landslide prediction. However, this improvement has been mostly devoted to models that estimate locations where landslides may occur, i.e., landslide susceptibility. Although susceptibility is crucial when assessing landslide hazard, another equally important piece of information is the potential landslide area once landslides initiate on a given slope.This manuscript addresses this gap in the literature by using a Generalized Additive Model whose target variable is the topographically-corrected landslide areal extent at the slope unit level. In our case, the underlying assumption is that the variability of landslide areas across the geographic space follows a Log-Normal probability distribution. We test this framework on co-seismic landslides triggered by the Kaikōura earthquake (7.8 Mw on November 13th 2016). The model performance was evaluated using random and spatial cross-validation. Additionally, we simulated the expected landslide area over slopes in the entire study area, including those that did not experience slope failures in the past.The performance scores revealed a moderate to strong correlation between the observed and predicted landslide areas. Moreover, the simulations show coherent patterns, suggesting that it is worth extending the landslide area prediction further. We share data and codes in a GitHub repository https://github.com/mmorenoz/GAM_LandslideSize to promote the repeatability and reproducibility of this research.

Predicting Earthquake-Induced Landslides by Using a Stochastic Modeling Approach: A Case Study of the 2001 El Salvador Coseismic Landslides

In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images allowed us to map 6491 coseismic landslides, mainly debris slides and flows that occurred in volcanic epiclastites and pyroclastites. Four different multivariate adaptive regression splines (MARS) models were produced using different predictors and landslide inventories which contain slope failures triggered by an extreme rainfall event in 2009 and those induced by the earthquakes of 2001. In a predictive analysis, three validation scenarios were employed: the first and the second included 25% and 95% of the landslides, respectively, while the third was based on a k-fold spatial cross-validation. The results of our analysis revealed that: (i) the MARS algorithm provides reliable predictions of coseismic landslides; (ii) a better ability to predict coseismic slope failures was observed when including susceptibility to rainfall-triggered landslides as an independent variable; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) an incomplete inventory of coseismic slope failures built just after the earthquake event can be used to identify potential locations of yet unreported landslides.

Coseismic landslides triggered by the 2022 Luding Ms6.8 earthquake, China

Coseismic landslides triggered by the 2022 Luding Ms6.8 earthquake, China

Location and Activity Changes of Slow-Moving Landslides Due to an Earthquake: Perspective from InSAR Observations

Strong earthquakes can not only trigger many landslides in a short period of time but can also change the stability of slopes in the earthquake area, causing them to be active for a long time after the earthquake. Research on the variation of slow-motion slopes before and after earthquakes can help us to better understand the mechanism of earthquake-affected landslides, which is also crucial for assessing the long-term landslide risk in seismically active areas. Here, L-band ALOS-2 PALSAR-2 images are utilized with the SBAS-InSAR algorithm to monitor and assess the location and activity changes of slow-moving landslides in the Iburi region (Hokkaido, Japan) before and after an earthquake occurred on 6 September 2018. Unlike previous studies, which focused on single typical landslides, we tracked all the landslides within a 33 × 55 km region close to the epicenter. According to the results, the majority of the co-seismic landslides that quickly failed during the earthquake are now stable, and a few of them are still moving. In contrast, due to near-field seismic shaking, certain slopes that did not show substantial surface changes during the earthquake period continued to move and eventually developed into slow-moving landslides. In addition, it can be seen from the spatial distribution of slow-moving landslides after the earthquake that this distribution is not only dependent on strong earthquake seismic vibration or the hanging-wall effect. Far-field weak vibrations can also accelerate landslides. Additionally, we discovered that the earthquake made the unstable slopes move more quickly but also tended to stabilize the slopes that were already in motion before the earthquake. The various response modes of slow-moving landslides to seismic events are related not only to the intensity of seismic vibration but also to the geological conditions of the region and to the size of the landslide itself. These findings are extremely valuable for studying the mechanism of earthquake-affected landslides.

Integrating the SLIDE model within CAESAR-Lisflood: Modeling the ‘rainfall-landslide-flash flood’ disaster chain mechanism under landscape evolution in a mountainous area

The interaction mechanism between the dynamically changing environment and the subsequent natural hazards in the years following an earthquake has been studied based on a multi-temporal remote sensing dataset and continually measured data. However, how to quantitatively model this interaction mechanism has not been fully addressed. In this study, we first modeled the interaction mechanism between the ‘rainfall-landslide-flash flood’ disaster chain and the dynamically changing environment in mountainous areas by incorporating the SLope-Infiltration-Distributed Equilibrium (SLIDE) model within the landscape evolution model (CAESAR-Lisflood) and then applied the integrated CAESAR-Lisflood model in a Wenchuan earthquake-stricken area. The results demonstrated that the landslide susceptibility under extreme rainfall can be predicted effectively based on the integrated CAESAR-Lisflood model and that new/enlarged landslides occur more easily in mountain valleys, near the valley outlet, and in the main steep gullies. Most of the high landslide susceptibility areas are not located in coseismic landslide areas with high vegetation recovery. The landslide legacy effects had a significant influence on the landscape erosion and deposition processes and a great effect on the spatial distribution pattern of the material redistribution in the basin, which in turn affected the subsequent disaster occurrence. The integrated CAESAR-Lisflood model compensates for the effect of the “rainfall-landslide-flash flood” disaster chain on the process of erosion and deposition, improves the model’s applicability in earthquake-stricken areas, and provides scientific information for regional disaster management and reduction.

Coseismic landslide sediment increased by the “9.5” Luding earthquake, Sichuan, China

Coseismic landslide sediment increased by the “9.5” Luding earthquake, Sichuan, China

Spatial Pattern and Intensity Mapping of Coseismic Landslides Triggered by the 2022 Luding Earthquake in China

On 5 September 2022, an Mw 6.6 earthquake occurred in Luding County in China, resulting in extensive surface rupture and casualties. Sufficient study on distribution characteristics and susceptibility regionalization of the earthquake-induced disasters (especially coseismic landslides) in the region has great significance to mitigation of seismic hazards. In this study, a complete coseismic landslide inventory, including 6233 landslides with 32.4 km2 in area, was present through multi-temporal satellite images. We explored the distribution and controlling conditions of coseismic landslides induced by the 2022 Luding event from the perspective of epicentral distance. According to the maximum value of landslide area density, the geographical location with the strongest coseismic landslide activity intensity under the influence of seismic energy, the macro-epicenter, was determined, and we found a remarkable relationship with the landslide distribution and macro-epicentral distance, that is, both the landslide area and number density associatively decreased with the increase in macro-epicentral distance. Then, a fast and effective method for coseismic landslide intensity zoning based on the obvious attenuation relationship was proposed, which could provide theoretical reference for susceptibility mapping of coseismic landslides induced by earthquakes in mountainous areas. Additionally, to quantitatively assess the impact of topographic, seismogenic and lithological factors on the spatial pattern of coseismic landslides, the relationships between the occurrences of coseismic landslides and influencing factors, i.e., elevation, slope angle, local relief, aspect, distance to fault and lithology, were examined. This study provides a fresh perspective on intensity zoning of coseismic landslides and has important guiding significance for post-earthquake reconstruction and land use in the disaster area.

Semantic Segmentation Model for Wide-Area Coseismic Landslide Extraction Based on Embedded Multichannel Spectral–Topographic Feature Fusion: A Case Study of the Jiuzhaigou Ms7.0 Earthquake in Sichuan, China

The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. However, the identification efficiency is low, which seriously delays the earthquake emergency response. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis, object-oriented analysis, change detection, and machine learning. However, the effectiveness of coseismic landslide extraction was low in wide areas with complex topographic and geomorphic backgrounds. Therefore, this paper offers a comprehensive study of the factors influencing coseismic landslides and researches rapid and accurate wide-area coseismic landslide extraction methods with multisource remote sensing and geoscience technology. These techniques include digital elevation modeling (DEM) and its derived slopes and aspects. An embedded multichannel spectral–topographic feature fusion model for coseismic landslide extraction based on DeepLab V3+ is proposed, and a knowledge-enhanced deep learning information extraction method integrating geological knowledge is formed. Using the Jiuzhaigou Ms7.0 earthquake (seismic intensity VIII) in Sichuan Province, China, a comparison of landslide extraction models and strategies is carried out. The results show that the model proposed in this paper achieves the best balance in the accuracy and efficiency of wide-area extractions. Using multiple feature data of coseismic landslides, the problem of mixed pixels is solved. The rate of the misidentification of landslides as clouds, snow, buildings, and roads is significantly lower than in other methods. The identified landslide boundaries are smoother and more accurate, and the connectivity is better. Compared with other methods, ours can more accurately eliminate landslides not triggered by the Jiuzhaigou earthquake. While using the image block strategy to ensure extraction efficiency, it also improves the extraction accuracy of wide-area coseismic landslides in complex backgrounds.

A Novel Deep Learning Method for Automatic Recognition of Coseismic Landslides

Massive earthquakes generally trigger thousands of coseismic landslides. The automatic recognition of these numerous landslides has provided crucial support for post-earthquake emergency rescue, landslide risk mitigation, and city reconstruction. The automatic recognition of coseismic landslides has always been a difficult problem due to the relatively small size of a landslide and various complicated environmental backgrounds. This work proposes a novel semantic segmentation network, EGCN, to improve the landslide identification accuracy. EGCN conducts coseismic landslide recognition by a recognition index set as the input data, CGBlock as the basic module, and U-Net as the baseline. The CGBlock module can extract the relatively stable global context-dependent features (global context features) and the unstable local features by the GNN Branch and CNN Branch (GNN Branch contains the proposed EISGNN) and integrates them via adaptive weights. This method has four advantages. (1) The recognition indices are established according to the causal mechanism of coseismic landslides. The rationality of the indices guarantees the accuracy of landslide recognition. (2) The module of EISGNN is suggested based on the entropy importance coefficient and GATv2. Owing to the feature aggregation among nodes with high entropy importance, global and useful context dependency can be synthesized and the false alarm of landslide recognition can be reduced. (3) CGBlock automatically integrates context features and local spatial features, and has strong adaptability for the recognition of coseismic landslides located in different environments. (4) Owing to CGBlock being the basic module and U-Net being the baseline, EGCN can integrate the context features and local spatial characteristics at both high and low levels. Thus, the accuracy of landslide recognition can be improved. The meizoseismal region of the Ms 7.0 Jiuzhaigou earthquake is selected as an example to conduct coseismic landslide recognition. The values of the precision indices of Overall Accuracy, mIoU, Kappa, F1-score, Precision, and Recall reached 0.99854, 0.99709, 0.97321, 0.97396, 0.97344, and 0.97422, respectively. The proposed method outperforms the current major deep learning methods.

A regional scale coseismic landslide analysis framework: Integrating physics-based simulation with flexible sliding analysis

This paper describes a framework for regional scale coseismic landslide evaluation that combines physics-based wave propagation simulation at a regional scale, with flexible sliding analysis at a site scale. The physics-based simulation incorporates fault rupture process, complex topography and dynamic site response. The sliding displacements of flexible masses are calculated by integrating seismic parameter k obtained from the physics-based simulation over seismic resistance.The framework is applied to the case study of coseismic landslides during the 2016 Mw 7.0 Kumamoto earthquake in Japan, which has well documented fault mechanism, geologic information and landslide inventory. The performance of the model prediction is evaluated by receiver operating characteristic (ROC) analysis. This study highlights the near-fault effect and soil nonlinear effect on landslide distribution, and it is demonstrated that the landslide prediction can be notably improved with the consideration of topographic amplification of ground motions. Empirical correlations between topographic amplification of key intensity measures and parameterized topographic features are developed. Overall, the simulation captures 58% of observed landslides in the inventory (using a displacement threshold of 15 cm), showing the proposed framework is a promising tool for regional coseismic landslide analysis.

Analysis of spatial distribution of landslides triggered by the Ms 6.8 Luding earthquake in China on September 5, 2022

BackgroundOn September 5, 2022, an Ms 6.8 earthquake occurred in Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan, China. The casualties and economic losses caused by the earthquake are huge. Most of the landslides triggered by this earthquake are small and medium-sized rock collapses and destructive debris flows, with a small number of large-scale landslides.ResultsThere are 5007 coseismic landslides in the Luding earthquake VII-degree area and above, with a total area of 17.36 km2. The landslides are generally distributed along the NW-SE direction. The highest landslide area density is 13.8%, and the highest point density of the landslide is 35.73 km2. High-density area is mainly concentrated in the IX-degree area and the northeast side of the seismogenic fault. Coseismic landslides are more likely to develop in the area with the slope more than 40°, the slope direction of 67.5-112.5 degree, and vegetation coverage of 40–50%. More landslides are developed in granite areas and forest areas. The closer distance to active faults, the more the coseismic landslides develop. The correlation between coseismic landslide development and the distance from the active faults is better than that between coseismic landslide development and the distance from the seismogenic faults.ConclusionThe landslide database used in this paper is the most perfect at present, and the research results have also been verified by fieldwork. This is also the first applied study of this landslide inventory, which indirectly confirmed the accuracy of the inventory. The results obtained in the manuscript will provide an important guideline for futuredisaster prevention and mitigation in southwest China.

Mechanical properties and acoustic emission response of carbonate fault breccias: a study from Greece

The present study aims to investigate the mechanical properties and acoustic emission (AE) response of carbonate fault rocks in Lefkada Island, Greece. In the aforementioned region, tectonic processes have resulted in the formation of fault breccias with poor mechanical properties and complex behaviour. In these weak formations, conventional laboratory testing methods are restricted due to disintegration during sampling and specimen preparation. A novel sample preparation and laboratory testing technique are used jointly with a back-analysis of a co-seismic landslide that occurred during a M w 6.5 earthquake in 2015. These two approaches are employed to characterize and predict the mechanical behaviour of these formations. The uniaxial compressive strength (UCS) of the fault breccias, determined in the laboratory, has a wide range due primarily to the complex nature of the material. The back-analysis, using a 3D limit equilibrium method, proves that the shear strength of the in situ material is significantly lower than that determined in the laboratory. Finally, the acoustic emission response of fault breccias was different compared to that of brittle rocks. Both b -value analysis and the damage variable evolution indicate that the accumulated damage within fault breccias is higher than expected, and thus they are more prone to fatigue. Thematic collection: This article is part of the Leading to Innovative Engineering Geology Practices collection available at: https://www.lyellcollection.org/topic/collections/leading-to-innovative-engineering-geology-practices

The structure of a magnitude-frequency relation for debris flows conditioned by earthquake shock

Debris flow is a major secondary geological process associated with the 2008 M7.9 Wenchuan Earthquake area, causing the loss of around 200 lives as well as extensive property damage. Based on data developed over the past 21 years (1998–2018) of continuous monitoring of debris flow events in Sichuan, the temporal and spatial aspects of debris flows have been analyzed, including magnitude and frequency. The 21-year study period is divided into three time-intervals: 1998–2007 (pre-earthquake), 2008–2013 (short-term post-earthquake), and 2014–2018 (long-term post-earthquake). Distribution maps are plotted to compare debris flow distribution density in the three periods and to visualize debris flow development throughout Sichuan Province from 1998 to 2018. Many large-scale debris flows occur in groups immediately following the earthquake (2008–2013). After 2014, the magnitude and frequency of debris flows begin to decrease and gradually returned near to the pre-earthquake level as the damaged landscape recovered. The effect of rainfall is investigated by analyzing a 21-year record of precipitation; Following the earthquake the threshold rainfall values for debris flow triggering decrease only to recover to pre-earthquake levels as the landscape heals. Magnitude-frequency (M-F) relations for debris flows of the three periods were developed, and the distribution can be well fitted by a power-law function. Finally, the work done by debris flow was defined by multiplying the magnitude by frequency. At the regional scale, the work peaks in the three time periods have been determined and it is found that the peaks corresponded with the larger debris flow sizes. The concept of offset is introduced to describe the shift of the work peak in relation to the probability peak and is termed the Wolman-Miller offset. It is concluded that with a greater offset in the short-term post-earthquake period, more work has been done by larger events and that the greater proportion of the volume being mobilized by larger events occurring more frequently is a key process in the landscape returning to a pre-earthquake (equilibrium) state. The recovery of an earthquake-damaged landscape represents a transient period of high hazard that should be considered in any hazard assessment of earthquake-triggered landslides (in addition to co-seismic landslides).

Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data

Mapping of landslides over space has seen an increasing attention and good results in the last decade. While current methods are chiefly applied to generate event-inventories, whereas multi-temporal (MT) inventories are rare, even using manual landslide mapping. Here, we present an innovative deep learning strategy which employs transfer learning that allows for the Attention Deep Supervision Multi-Scale U-Net model to be adapted for landslide detection tasks in new areas. The method also provides the flexibility of re-training a pretrained model to detect both rainfall- and earthquake-triggered landslides on new target areas. For the mapping, we used archived Planet Lab remote sensing images spanning a period between 2009 till 2021 with spatial resolution of 3–5 m to systematically generate MT landslide inventories. When we examined all cases, our approach provided an average F1 score of 0.8 indicating that we successfully identified the spatiotemporal occurrences of landslides. To examine the size distribution of mapped landslides we compared the frequency-area distributions of predicted co-seismic landslides with manually mapped products from the literature. Results showed a good match between calculated power-law exponents where the difference ranges between 0.04 and 0.21. Overall, this study showed that the proposed algorithm could be applied to large areas to generate polygon-based MT landslide inventories.