Landslides and dam damage resulting from the Jiuzhaigou earthquake (8 August 2017), Sichuan, China

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.

Damage amplification during repetitive seismic waves in mechanically loaded rocks

Cycles of stress build-up and release are inherent to tectonically active planets. Such stress oscillations impart strain and damage, prompting mechanically loaded rocks and materials to fail. Here, we investigate, under uniaxial conditions, damage accumulation and weakening caused by time-dependent creep (at 60, 65, and 70% of the rocks' expected failure stress) and repeating stress oscillations (of ± 2.5, 5.0 or 7.5% of the creep load), simulating earthquakes at a shaking frequency of ~ 1.3 Hz in volcanic rocks. The results show that stress oscillations impart more damage than constant loads, occasionally prompting sample failure. The magnitudes of the creep stresses and stress oscillations correlate with the mechanical responses of our porphyritic andesites, implicating progressive microcracking as the cause of permanent inelastic strain. Microstructural investigation reveals longer fractures and higher fracture density in the post-experimental rock. We deconvolve the inelastic strain signal caused by creep deformation to quantify the amount of damage imparted by each individual oscillation event, showing that the magnitude of strain is generally largest with the first few oscillations; in instances where pre-existing damage and/or the oscillations' amplitude favour the coalescence of micro-cracks towards system scale failure, the strain signal recorded shows a sharp increase as the number of oscillations increases, regardless of the creep condition. We conclude that repetitive stress oscillations during earthquakes can amplify the amount of damage in otherwise mechanically loaded materials, thus accentuating their weakening, a process that may affect natural or engineered structures. We specifically discuss volcanic scenarios without wholesale failure, where stress oscillations may generate damage, which could, for example, alter pore fluid pathways, modify stress distribution and affect future vulnerability to rupture and associated hazards.

Spatio-temporal evolution of post-seismic landslides and debris flows: 2017 Ms 7.0 Jiuzhaigou earthquake

Spatio-temporal evolution of post-seismic landslides and debris flows provides a new perspective to understand post-earthquake evolution of geological environments and landscapes, and to instruct cascaded catastrophic hazard mitigation and post-disaster reconstruction. However, limited earthquake events have been investigated for post-earthquake geohazard evolution. This work reports the geohazard evolution after the 2017 M_s 7.0 Jiuzhaigou earthquake considering the effects of the earthquake, geology, terrain, meteorology, hydrology, and human engineering activity. Some new viewpoints are suggested. (1) Landslide and debris flow activity intensified in the first year following the earthquake under the effects of the antecedent earthquake, precipitation, fault tectonics, human engineering activity, and fluvial networks. (2) Landslide and debris flow activity declined rapidly in the second year as a result of dramatically reduced sediments, declined rainfall, and self-healed slopes. (3) The significant decay of landslide and debris flow activity and the prominent reduction of loose deposits indicate that the geological environment was gradually restoring. (4) Although the hazard effect mitigation and geological environment restoration were ongoing (in the absence of rainstorm events) to attain a new balance, the geoenvironment has not returned to the pre-earthquake level because of widespread unrecovered geohazards and the remaining loose deposits on hillslopes or in channels. (5) The geological environment after the Jiuzhaigou earthquake may re-equilibrate and return to the pre-earthquake level more quickly than after the Kashmir, Chi-Chi, Gorkha, Wenchuan, and Murchison earthquakes. This work provides new knowledge pertaining to geohazard evolution after a strong earthquake and to profound impacts of a catastrophic earthquake on geological environment and landscape.

Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery

This paper proposes a new approach based on an unsupervised deep learning (DL) model for landslide detection. Recently, supervised DL models using convolutional neural networks (CNN) have been widely studied for landslide detection. Even though these models provide robust performance and reliable results, they depend highly on a large labeled dataset for their training step. As an alternative, in this paper, we developed an unsupervised learning model by employing a convolutional auto-encoder (CAE) to deal with the problem of limited labeled data for training. The CAE was used to learn and extract the abstract and high-level features without using training data. To assess the performance of the proposed approach, we used Sentinel-2 imagery and a digital elevation model (DEM) to map landslides in three different case studies in India, China, and Taiwan. Using minimum noise fraction (MNF) transformation, we reduced the multispectral dimension to three features containing more than 80% of scene information. Next, these features were stacked with slope data and NDVI as inputs to the CAE model. The Huber reconstruction loss was used to evaluate the inputs. We achieved reconstruction losses ranging from 0.10 to 0.147 for the MNF features, slope, and NDVI stack for all three study areas. The mini-batch K-means clustering method was used to cluster the features into two to five classes. To evaluate the impact of deep features on landslide detection, we first clustered a stack of MNF features, slope, and NDVI, then the same ones plus with the deep features. For all cases, clustering based on deep features provided the highest precision, recall, F1-score, and mean intersection over the union in landslide detection.

A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan)

Earthquakes and heavy rainfalls are the two leading causes of landslides around the world. Since they often occur across large areas, landslide detection requires rapid and reliable automatic detection approaches. Currently, deep learning (DL) approaches, especially different convolutional neural network and fully convolutional network (FCN) algorithms, are reliably achieving cutting-edge accuracies in automatic landslide detection. However, these successful applications of various DL approaches have thus far been based on very high resolution satellite images (e.g., GeoEye and WorldView), making it easier to achieve such high detection performances. In this study, we use freely available Sentinel-2 data and ALOS digital elevation model to investigate the application of two well-known FCN algorithms, namely the U-Net and residual U-Net (or so-called ResU-Net), for landslide detection. To our knowledge, this is the first application of FCN for landslide detection only from freely available data. We adapt the algorithms to the specific aim of landslide detection, then train and test with data from three different case study areas located in Western Taitung County (Taiwan), Shuzheng Valley (China), and Eastern Iburi (Japan). We characterize three different window size sample patches to train the algorithms. Our results also contain a comprehensive transferability assessment achieved through different training and testing scenarios in the three case studies. The highest f1-score value of 73.32% was obtained by ResU-Net, trained with a dataset from Japan, and tested on China's holdout testing area using the sample patch size of 64 × 64 pixels.

Characteristics of a Debris Flow Disaster and Its Mitigation Countermeasures in Zechawa Gully, Jiuzhaigou Valley, China

On 8 August 2017, an Ms 7.0 earthquake struck Jiuzhaigou Valley, triggering abundant landslides and providing a huge source of material for potential debris flows. After the earthquake debris flows were triggered by heavy rainfall, causing traffic disruption and serious property losses. This study aims to describe the debris flow events in Zechawa Gully, calculate the peak discharges of the debris flows, characterize the debris flow disasters, propose mitigation countermeasures to control these disasters and analyse the effectiveness of countermeasures that were implemented in May 2019. The results showed the following: (1) The frequency of the debris flows in Zechawa Gully with small- and medium-scale will increase due to the influence of the Ms 7.0 Jiuzhaigou earthquake. (2) An accurate debris flow peak discharge can be obtained by comparing the calculated results of four different methods. (3) The failure of a check dam in the channel had an amplification effect on the peak discharge, resulting in a destructive debris flow event on 4 August 2016. Due to the disaster risk posed by dam failure, both blocking and deposit stopping measures should be adopted for debris flow mitigation. (4) Optimized engineering countermeasures with blocking and deposit stopping measures were proposed and implemented in May 2019 based on the debris flow disaster characteristics of Zechawa Gully, and the reconstructed engineering projects were effective in controlling a post-earthquake debris flow disaster on 21 June 2019.

Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model

Seismic landslides are the most common and highly destructive earthquake-triggered geological hazards. They are large in scale and occur simultaneously in many places. Therefore, obtaining landslide information quickly after an earthquake is the key to disaster mitigation and relief. The survey results show that most of the landslide-information extraction methods involve too much manual participation, resulting in a low degree of automation and the inability to provide effective information for earthquake rescue in time. In order to solve the abovementioned problems and improve the efficiency of landslide identification, this paper proposes an automatic landslide identification method named improved U-Net model. The intelligent extraction of post-earthquake landslide information is realized through the automatic extraction of hierarchical features. The main innovations of this paper include the following: (1) On the basis of the three RGB bands, three new bands, DSM, slope, and aspect, with spatial information are added, and the number of feature parameters of the training samples is increased. (2) The U-Net model structure is rebuilt by adding residual learning units during the up-sampling and down-sampling processes, to solve the problem that the traditional U-Net model cannot fully extract the characteristics of the six-channel landslide for its shallow structure. At the end of the paper, the new method is used in Jiuzhaigou County, Sichuan Province, China. The results show that the accuracy of the new method is 91.3%, which is 13.8% higher than the traditional U-Net model. It is proved that the new method is effective and feasible for the automatic extraction of post-earthquake landslides.

A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests

Despite recent progress in landslide susceptibility mapping, a holistic method is still needed to integrate and customize influential factors with the focus on forest regions. This study was accomplished to test the performance of geographic object-based random forest in modeling the susceptibility of protected and non-protected forests to landslides in northeast Iran. Moreover, it investigated the influential conditioning and triggering factors that control the susceptibility of these two forest areas to landslides. After surveying the landslide events, segment objects were generated from the Landsat 8 multispectral images and digital elevation model (DEM) data. The features of conditioning factors were derived from the DEM and available thematic layers. Natural triggering factors were derived from the historical events of rainfall, floods, and earthquake. The object-based image analysis was used for deriving anthropogenic-induced forest loss and fragmentation. The layers of logging and mining were obtained from available historical data. Landslide samples were extracted from field observations, satellite images, and available database. A single database was generated including all conditioning and triggering object features, and landslide samples for modeling the susceptibility of two forest areas to landslides using the random forest algorithm. The optimal performance of random forest was obtained after building 500 trees with the area under the receiver operating characteristics (AUROC) values of 86.3 and 81.8% for the protected and non-protected forests, respectively. The top influential factors were the topographic and hydrologic features for mapping landslide susceptibility in the protected forest. However, the scores were loaded evenly among the topographic, hydrologic, natural, and anthropogenic triggers in the non-protected forest. The topographic features obtained about 60% of the importance values with the domination of the topographic ruggedness index and slope in the protected forest. Although the importance of topographic features was reduced to 36% in the non-protected forest, anthropogenic and natural triggering factors remarkably gained 33.4% of the importance values in this area. This study confirms that some anthropogenic activities such as forest fragmentation and logging significantly intensified the susceptibility of the non-protected forest to landslides.

Large landslides at the northeastern margin of the Bayan Har Block, Tibetan Plateau, China

Large landslides (volume greater than or equal to 10⁶ m³) usually have disastrous consequences and clearly influence the evolution of the local landscape. In this study, a detailed investigation of large landslides, across 20 towns over an area of 5000 km², was carried out on the northeastern margin of the Bayan Har Block, at the eastern margin of the Tibetan Plateau, China. The results show that there are 129 large landslides in this area. Among them, 79 landslides have volumes within 10⁶-10⁷ m³, 52 landslides have volumes within 10⁷-10⁸ m³ and 2 landslides have volumes larger than 10⁸ m³. Most of these landslides are distributed along rivers, and more than 32% are densely concentrated in three small regions. The landslides mainly occur in high slopes and exhibit obvious sturzstrom characteristics. Analysis of the factors controlling landslide occurrence shows that elevation, slope angle, slope aspect, lithology, faults and rivers (valley) clearly influence landslide occurrence, while rainfall has no obvious influence. Earthquakes are considered an important trigger of and contributor to landslide occurrence.