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Earthquake Triggered Landslides (ETLs) are serious secondary hazards of earthquakes, causing severe casualties and property losses, and their rapid, automated and accurate detection is of great value. Due to the all-day and all-weather imaging capability of Synthetic Aperture Radar (SAR), ETL detection using SAR images is promising but faces the problem of insufficient accuracy. In this study, we proposed a deep learning-based ETL detection method to address this problem. Firstly, published ETL inventories and SAR images are combined to generate high-quality training datasets. Then, a landslide detection network of Multi-level Features Effective Weighting and Fusion (MFEWF) is proposed to effectively extract and fuze multi-level landslide features to identify SAR pixels within ETLs. Finally, the ETL boundary is determined based on these identified SAR pixels. This method is verified through three earthquake cases: the 2017 Mainling, China earthquake, the 2018 Palu, Indonesia earthquake and the 2018 Papua New Guinea earthquake. Results show that our method can effectively identify landslide boundaries with high accuracy (88.8%, 81.4% and 82.4% for the three cases), obviously outperforming other deep learning frameworks (e.g. DeepLabV3+). Using Sentinel-1 imagery to achieve such high accuracy in landslide detection, this study will improve emergency response to landslide disasters following earthquakes.

Different soil cover saturation has a significant effect in influencing slope stability conditions of weathered covers under earthquake-induced shaking. Here we analyze the Montecilfone, Italy (2018), case history, an Mw 5.1 earthquake that revealed an exceptionality in the spatial distribution of the surveyed earthquake-induced shallow landslides. This feature can be justified as intense rainfall occurred in the epicentral area before the seismic event, contributing to increasing the saturation and the weight of the soil covers. To verify the effective influence of antecedent rainfall as a preparatory factor in the earthquake triggering of soil covers, stability conditions for both static and dynamic scenarios were validated by reconstructing different saturation conditions related to a rainfall event that occurred before the earthquake. Soil cover surveying was performed within a 150 km2 area to output its spatial distribution in terms of their compositional features and thickness, whose variability was constrained through empirical models. Based on laboratory test results, 1D infiltration numerical models were performed through the Hydrus-1D free domain software to estimate the saturation degree of the soil cover and the water infiltration depth, taking as a reference the intensity of the rainfall event. Soil cover sequential charts of water content were obtained at different depths and times up to those recorded at the time of earthquake occurrence by the performed numerical modelling. Safety factors (SFs) of the slope covers were quantified assuming an unsaturated condition in the slope stability equation. The outputs reveal that pore pressure spatial distribution in the unsaturated medium infers on the earthquake-induced scenario of shallow landsliding, demonstrating its role as a preparatory factor for earthquake-induced shallow landslides. [ABSTRACT FROM AUTHOR]

Different soil cover saturation has a significant effect in influencing slope stability conditions of weathered covers under earthquake-induced shaking. Here we analyze the Montecilfone, Italy (2018), case history, an Mw 5.1 earthquake that revealed an exceptionality in the spatial distribution of the surveyed earthquake-induced shallow landslides. This feature can be justified as intense rainfall occurred in the epicentral area before the seismic event, contributing to increasing the saturation and the weight of the soil covers. To verify the effective influence of antecedent rainfall as a preparatory factor in the earthquake triggering of soil covers, stability conditions for both static and dynamic scenarios were validated by reconstructing different saturation conditions related to a rainfall event that occurred before the earthquake. Soil cover surveying was performed within a 150 km2 area to output its spatial distribution in terms of their compositional features and thickness, whose variability was constrained through empirical models. Based on laboratory test results, 1D infiltration numerical models were performed through the Hydrus-1D free domain software to estimate the saturation degree of the soil cover and the water infiltration depth, taking as a reference the intensity of the rainfall event. Soil cover sequential charts of water content were obtained at different depths and times up to those recorded at the time of earthquake occurrence by the performed numerical modelling. Safety factors (SFs) of the slope covers were quantified assuming an unsaturated condition in the slope stability equation. The outputs reveal that pore pressure spatial distribution in the unsaturated medium infers on the earthquake-induced scenario of shallow landsliding, demonstrating its role as a preparatory factor for earthquake-induced shallow landslides.

Inventory of pre-earthquake and earthquake triggered landslides is used to provide insight into the interplay between climatic and tectonic forcing in the development of the rock slopes of the Skolis Mountain, in the NW Peloponnese. Aerial photograph analysis and surface mapping indicate that the Skolis Mountain is characterized by long-term climatically and tectonically controlled rock falls forming taluses. Temporally these taluses show a slow progressive inflation in surface area from 1945 to 2007. However, the post-earthquake surface area of the rock falls increased three times. Similarly 75 rock fall sites before the earthquake, increased into 89 after the Movri Mountain earthquake (Mw 6.4). In addition, during the earthquake a series of isolated rock falls descended Skolis slopes causing threat of the Santomerion village and blocking significant part of the dirt roads around it. These boulders are clustered in three areas beyond the base of taluses. The rock slope failures are controlled by a complex array of discontinuities that are conveniently related to rock mass classification following the geological strength index. These discontinuities are associated with joints and faults caused during the formation of the Hellenides fold- and thrust-belt, and/or related tectonic damage. We infer that a dense pattern of fractures in limestone plays a crucial role in the reactivation of movement within the rock falls during the 2008 Movri Mountain earthquake. All these data are used to define two borders, the taluses base and the rock fall hazard border beyond the base of taluses. For defining these borders we use the angle β drawn from the boulders' release zone down its maximal runout points. Our results indicate that the border defined by the β = 33° corresponds to the climatically driven rock falls while the β = 24° border is defined as the boulders' maximum runout during earthquakes. [ABSTRACT FROM AUTHOR]

Abstract: The main purpose of this study is to compare the following six GIS-based models for susceptibility mapping of earthquake triggered landslides: bivariate statistics (BS), logistic regression (LR), artificial neural networks (ANN), and three types of support vector machine (SVM) models that use the three different kernel functions linear, polynomial, and radial basis. The models are applied in a tributary watershed of the Fu River, a tributary of the Jialing River, which is part of the area of China affected by the May 12, 2008 Wenchuan earthquake. For this purpose, eleven thematic data layers are used: landslide inventory, slope angle, aspect, elevation, curvature, distance from drainages, topographic wetness index (TWI), distance from main roads, distance from surface rupture, peak ground acceleration (PGA), and lithology. The data layers were specifically constructed for analysis in this study. In the subsequent stage of the study, susceptibility maps were produced using the six models and the same input for each one. The validations of the resulting susceptibility maps were performed and compared by means of two values of area under curve (AUC) that represent the respective success rates and prediction rates. The AUC values obtained from all six results showed that the LR model provides the highest success rate (AUC=80.34) and the highest prediction rate (AUC=80.27). The SVM (radial basis function) model generates the second-highest success rate (AUC=80.302) and the second-highest prediction rate (AUC=80.151), which are close to the value from the LR model. The results using the SVM (linear) model show the lowest AUC values. The AUC values from the SVM (linear) model are only 72.52 (success rates) and 72.533 (prediction rates). Furthermore, the results also show that the radial basis function is the most appropriate kernel function of the three kernel functions applied using the SVM model for susceptibility mapping of earthquake triggered landslides in the study area. The paper also provides a counter-example for the widely held notion that validation performances of the results from application of the models obtained from soft computing techniques (such as ANN and SVM) are higher than those from applications of LR and BA models. [Copyright &y& Elsevier]