The world’s second-largest, recorded landslide event: lessons learnt from the landslides triggered during and after the 2018 Mw 7.5 Papua New Guinea earthquake

Widespread landslide events provide rare but valuable opportunities to investigate the spatial and size distributions of landslides in relation to seismic, climatic, geological and morphological factors. This study presents a unique event inventory for the co-seismic landslides induced by the February 25, 2018 Mw 7.5 Papua New Guinea earthquake as well as its post-seismic counterparts including the landslides triggered by either aftershocks or succeeding rainfall events that occurred between February 26 and March 19. We mapped approximately 11,500 landslides of which more than 10,000 were triggered by the mainshock with a total failed planimetric area of about 145 km2. Such a large area makes this inventory the world’s second-largest recorded landslide event after the 2008 Wenchuan earthquake. Large landslides are abundant throughout the study area located within the remote Papua New Guinea Highlands. Specifically, more than half of the landslide population is larger than 50,000 m2 and overall, post-seismic landslides are even larger than their co-seismic counterparts. Our analyses indicate that large and widespread landslides were triggered as a result of the compound effects of the strong seismicity, complex geology, steep topography and high rainfall. We statistically show that the 15-day antecedent precipitation, as a predisposing factor, contributes to the spatial distribution of co-seismic landslides. Also, we statistically demonstrate that the cumulative effect of aftershocks is the main factor disturbing steep hillslopes and causing the initiation of very large landslides up to the size of ~5 km2. Taking aside the role of the intense seismic swarm and antecedent precipitation, these inventories also provide evidence for landslide events where the active tectonics contribute to weaken hillslopes and the fatigue damage. Overall, the dataset and the findings provided by this paper is a step forward in seismic landslide hazard assessment of the entire Papua New Guinea mainland.

Analysis of factors affecting the Spatial Distribution of co-seismic landslides triggered by the 2011 (Mw 6.9) Sikkim earthquake

<p>The Himalayan region is known as an earthquake-triggered landslides prone area. It is characterized by high seismicity, large relative relief, steep slopes, and dense precipitation. These seismically triggered landslides are likely to affect substantial societal impacts, including loss of life, damage to houses, public buildings, various lifeline structures like highways, railways tracks, etc. Further, they obstruct post-earthquake emergency response efforts. A past study by Martha et al. 2014 reported that an earthquake of Mw 6.9 in 2011 triggered 1196 landslides in Sikkim which is a part of the eastern Himalayas. The slope failure events are controlled by several factors, which can be grouped into four main classes: seismology, topography, lithology, and hydrology. Each class contains several sub-factors. Having in-depth knowledge of these factors and their influence on the density of landslide events in the affected area due to the 2011 Sikkim earthquake is essential to realize the level of threat of co-seismic landslide due to future earthquakes. Eight landslide controlling factors is considered in this analysis including peak ground acceleration (PGA), slope, aspect, elevation, curvature, lithology, distance from rivers, and topographic wetness index (TWI). Further, the frequency ratio model using the GIS framework is applied to evaluate the contribution of each landslide controlling factor to landslide occurrence. Scatter plots between the number of landslides per km<sup>2</sup> (LN) and percentage of landslide area (LA) and causative factors indicate that distance from the river, slope angle, and PGA are the dominant factors that control the landslides. The results of the above analysis showed that the majority of co-seismic landslides occurred at slope >30°, preferably in East, Southeast, and South directions and near river within a distance of 1500 m. The detailed study of interactions among these factors can improve the understanding of the mechanisms of co-seismic landslide occurrence in Sikkim and will be useful for producing a co-seismic landslide susceptibility map of the area.</p>

Monitoring and Assessment for the Susceptibility of Landslide Changes After the 2017 Ms 7.0 Jiuzhaigou Earthquake Using the Remote Sensing Technology

Monitoring the change of post-seismic landslides could provide valuable information for geological disaster treatment. The 2017 Jiuzhaigou Ms 7.0 earthquake has triggered a large number of landslides in the Jiuzhaigou United Nations Educational, Scientific and Cultural Organization (UNESCO) Natural Heritage site, which provides a unique opportunity for monitoring the spatio-temporal characteristics and exploring the impact factors of post-seismic landslides change. In this study, the spatio-temporal characteristics of landslides and their post-seismic changes are analyzed using multi-source, multi-temporal, and multi-scale remote sensing data combining with the field study. The Support Vector Machine classification, visual interpretation, field investigation, and Geographic Information System technology are employed to extract landslides and analyze their spatial distribution patterns. Moreover, the Certainty Factor method is used to explore the susceptibility of landslides and to find key impact factors. Our results show that the net increase area of landslide is 1.2 km2 until September 27th, 2019, which are induced by the expansion of coseismic landslide, the post-seismic landslide, and the expansion of vegetation degradation. Moreover, the area expansion of the coseismic and post-seismic landslides is mainly related to the increase of debris flow induced by the post-seismic torrential rainfalls. The highest net increase rate of post-seismic landslide change does not distribute on the regions with the highest density of coseismic landslides. The susceptibility of post-seismic landslide change is greatly influenced by slope, altitude, aspect, peak ground acceleration fault, and strata. It is higher in the coseismic landslide area with low susceptibility. This study also suggests that the potential landslides will most likely occur in the unstable slope region affected by the additional driving force. Therefore, great attention should be paid to identify and prevent the potential landslides on unstable slopes in addition to treatments of the sliding slopes. This study provides a good example for the monitoring and assessment of post-seismic landslides in mountainous regions with a steep slope and deep valley.

Using Google Earth Engine to monitor co-seismic landslide recovery after the 2008 Wenchuan earthquake

Earthquake-triggered landslides can pose serious threats to mountain communities by remobilizing and providing loose materials for debris flows in post-seismic years. However, how long co-seismic landslides recover remains elusive due to limited observations. Using vegetation dynamics, we studied surface recovery of co-seismic landslides induced by the 2008 Wenchuan earthquake from May 2008 to July 2019 for over 20,000 km2. Landsat derived vegetation recovery on all co-seismic landslides has been assessed based on the Google Earth Engine, a cloud-based computing platform. We found most co-seismic landslides have been recovering after the earthquake but the spatial pattern is heterogeneous. The epicentre region with low elevations along the bottom of the Min River valley has the best landslide recovery, whereas many landslides on the high Longmen Mountain are poorly recovered ten years after the earthquake. These unrecovered hillslopes and gullies together with widespread loose debris indicate that surface processes on high mountains may still active and may provide source materials for debris flows, threatening communities at low elevations. To decipher possible mechanisms, we further analysed the relations between landslide recovery and twelve influencing factors, including slope, pre-seismic vegetation condition, landslide depth, landslide area, elevation, ground peak acceleration of the earthquake, aspect, slope curvatures, topographic positions, mean annual precipitation, ground cohesion strength and vegetation types. We found elevation, topographic position and pre-seismic vegetation condition are the most important factors that influence landslide recovery over all others. This work also demonstrates the efficiency of the Google Earth Engine for continuously monitoring landslide dynamics over large areas.

Automatic Extraction of Seismic Landslides in Large Areas with Complex Environments Based on Deep Learning: An Example of the 2018 Iburi Earthquake, Japan

After a major earthquake, the rapid identification and mapping of co-seismic landslides in the whole affected area is of great significance for emergency rescue and loss assessment of seismic hazards. In recent years, researchers have achieved good results in research on a small scale and single environment characteristics of this issue. However, for the whole earthquake-affected area with large scale and complex environments, the correct rate of extracting co-seismic landslides remains low, and there is no ideal method to solve this problem. In this paper, Planet Satellite images with a spatial resolution of 3 m are used to train a seismic landslide recognition model based on the deep learning method to carry out rapid and automatic extraction of landslides triggered by the 2018 Iburi earthquake, Japan. The study area is about 671.87 km2, of which 60% is used to train the model, and the remaining 40% is used to verify the accuracy of the model. The results show that most of the co-seismic landslides can be identified by this method. In this experiment, the verification precision of the model is 0.7965 and the F1 score is 0.8288. This method can intelligently identify and map landslides triggered by earthquakes from Planet images. It has strong practicability and high accuracy. It can provide assistance for earthquake emergency rescue and rapid disaster assessment.

Overtopping Risk Analysis of Earth Dams considering Effects of Failure Duration of Release Structures

For modern high earth dams, sufficient safety margin is considered in the designs of flood discharge capacity and dam crest elevation to prevent flood overtopping. However, for high earth dams which may induce catastrophic consequences, during their long operational period, extremely hazardous scenarios which could occur and threaten dam safety need to be considered. For the earth dams located in areas with intensive seismicity, there is a possible scenario that the release structures fail due to seismic landslides and gate failures caused by a severe earthquake when the flood begins to enter the reservoir. Thus, it is desirable to investigate the influence of failure duration of release structures on dam overtopping risk. Based on the Bayesian network, a methodology for overtopping risk analysis of earth dams considering effects of failure duration of release structures is proposed. The overtopping risk of the PBG earth-rockfill dam was analyzed to illustrate the methodology. The critical release structures which dominate the dam overtopping risk are identified. The dam overtopping risk is most sensitive to the failure duration of the spillway. The tolerable failure duration of the spillway is approximately 3 days, and when the failure duration of the spillway reaches 4 days, the dam overtopping risk drastically rises to an unacceptable level. The case study suggests that the proposed methodology could be helpful to analyze the influences of possible failure durations of release structures on dam overtopping risk and could facilitate preparation for emergency plans.