Earthquake-triggered 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.

A continuous 18.6–10 ka record of seismic events revealed by Xinmocun lacustrine sediments at Diexi, eastern Tibetan Plateau

Five Ms 7.0 earthquakes (Wenchuan, Yushu, Lushan, Jiuzhaigou and Maudo) have occurred on the eastern Tibetan Plateau since 2008, and > 20 historical earthquakes (Ms ≥ 5.0) have also been recorded, demonstrating the strong seismic activity prevalent in this region. Owing to the geomorphology of the region, consisting of alpine valleys with a lack of Quaternary deposits, it is difficult to conduct paleo-earthquake research through the excavation of trenches. Lacustrine sediments are regarded as the most suitable archives for paleo-earthquake studies because they often comprise a detailed, long-term, and continuous environmental record. To assess the seismic activity of the Diexi, we studied a well-exposed outcrop of Xinmocun lacustrine sediments that included three sections (XMCⅠ (11.5 m), XMCⅡ (5.6 m), and XMCⅢ (11 m)) near Diexi Lake, moving from a lake shore to lake center environment, at the intersection between the Minjiang fault and Songpinggou fault. Taking into account the geodynamic setting of the area and its known tectonic activity, soft-sediment deformation structures, including load and flame structures, injection structures, ball structures, pseudo-nodules, boudinage structures, plunged sediment mixture, liquefied breccia and micro-faults, discovered within the sections are interpreted as seismites. High-resolution grain-size and magnetic susceptibility indicators were used to obtain a continuous record of the changes in physical properties within the three sections enabling the recognition of additional seismic events that did not result in soft-sediment deformation structures. Rapidly deposited layers with underlying soft-sediment deformation structures in the sections record five (XMCI, XMCII and XMCIII) seismic events; these comprise siliciclastic-enriched sandy sediment fluxes from earthquake-triggered landslides or dust of siliciclastic-enriched clastic materials. Isolated rapidly deposited layers (without underlying soft-sediment deformation structures) record 18 (XMCII), 12 (XMCI), and 21 (XMCIII) additional earthquake-induced inputs of detrital material into the lake. Optically stimulated luminescence and 14C dating of the XMCⅠ, XMCⅡ, and XMCⅢ sections indicate continuous sediment deposition from 19.4–9.9 ka, 15.5–10.1 ka, and 18.7–10.6 ka, respectively, and the seismic events recorded in these sections can be cross-correlated within the age error range. Hence, there are a total of 30 seismic events recorded within the Xinmocun sediments during the period 18.6–10 ka, suggests a weakly periodic pattern with an apparent recurrence time of ∼ 280 years. The Minjiang fault and Songpinggou fault are Holocene active faults with a risk of causing strong earthquakes. Due to the Diexi area is located at the intersection of the Minjiang fault and Songpinggou fault, and has frequent seismic activity, suggest a high seismic risk in the future.

Updated Predictive Models for Permanent Seismic Displacement of Slopes for Greece and Their Effect on Probabilistic Landslide Hazard Assessment

Earthquake-triggered landslides have been widely recognized as a catastrophic hazard in mountainous regions. They may lead to direct consequences, such as property losses and casualties, as well as indirect consequences, such as disruption of the operation of lifeline infrastructures and delays in emergency response actions after earthquakes. Regional landslide hazard assessment is a useful tool to identify areas that are vulnerable to earthquake-induced slope instabilities and design prioritization schemes towards more detailed site-specific slope stability analyses. A widely used method to assess the seismic performance of slopes is by calculating the permanent downslope sliding displacement that is expected during ground shaking. Nathan M. Newmark was the first to propose a method to estimate the permanent displacement of a rigid body sliding on an inclined plane in 1965. The expected permanent displacement for a slope using the sliding block method is implemented by either selecting a suite of representative earthquake ground motions and computing the mean and standard deviation of the displacement or by using analytical equations that correlate the permanent displacement with ground motion intensity measures, the slope’s yield acceleration and seismological characteristics. Increased interest has been observed in the development of such empirical models using strong motion databases over the last decades. It has been almost a decade since the development of the latest empirical model for the prediction of permanent ground displacement for Greece. Since then, a significant amount of strong motion data have been collected. In the present study, several nonlinear regression-based empirical models are developed for the prediction of the permanent seismic displacements of slopes, including various ground motion intensity measures. Moreover, single-hidden layer Artificial Neural Network (ANN) models are developed to demonstrate their capability of simplifying the construction of empirical models. Finally, implementation of the produced modes based on Probabilistic Landslide Hazard Assessment is undertaken, and their effect on the resulting hazard curves is demonstrated and discussed.

Seismic landslide susceptibility assessment using principal component analysis and support vector machine

Seismic landslides are dangerous natural hazards that can cause immense damage to human lives and property. Susceptibility assessment of earthquake-triggered landslides provides the scientific basis and theoretical foundation for disaster emergency management in engineering projects. However, landslide susceptibility assessment requires a massive amount of historical landslide data. Evidence of past landslide activities may be lost due to changes in geographical conditions and human factors over time. The lack of landslide data poses difficulties in assessing landslide susceptibility. The aim of this study is to establish a generalized seismic landslide susceptibility assessment model for applying it to the Dayong highway in the Chenghai area, where earthquakes occur frequently but with a lack of landslide data. The landslide data used comes from the 2014 Ludian Ms (Surface wave magnitude) 6.5 earthquake in a region with geographical conditions similar to those in the Chenghai area. The influencing factors considered include elevation, slope, slope aspect, distance to streams, distance to faults, geology, terrain wetness index, normalized difference vegetation index, epicenter distance and peak ground acceleration. The frequency ratio method is used to eliminate influencing factors with poor statistical dispersion of landslides. Principal component analysis (PCA) is utilized to reduce the dimensionality of landslide conditioning factors and to improve the transferability of the assessment model to different regions. A support vector machine model is used to establish the susceptibility assessment model. The results show that the accuracy of the PCA–SVM model reaches 93.6%. The landslide susceptibility of the Chenghai area is classified into 5 classes, with the “Very high” landslide susceptibility class accounting for 0.63%. The 13-km section in the middle of the Dayong highway, which accounts for 8.9%, is identified as the high-risk area most obviously impacted by seismic landslides. This study provides a new approach for seismic landslide susceptibility assessment in areas lacking in landslide inventory data.

Analysis of Earthquake-Triggered Landslides through an Integrated Unmanned Aerial Vehicle-Based Approach: A Case Study from Central Italy

Landslides are one of the most hazardous(危险) secondary(二次) effects of earthquakes(地震) due(由于) to the potential(潜在) for large-scale(规模) damage(损伤) and long-term(学期) alterations to landscapes. During the 2016–2017 seismic sequence(序列) in Central Italy, many earthquake(地震)-triggered landslides (EQTLs) affected the road network(网络) and mountain trails. In this study, a methodological approach(方法) for analysing EQTLs, based on data(数据) derived from Unmanned Aerial Vehicle (UAV) surveys, is shown. The approach(方法) is applied to investigate(探讨) the geometric, structural(结构), geomechanical, and kinematic features of the Foce rockslide, which is introduced in the back analysis(分析). The investigation(调查) involved three main(主要) steps: (i) set up of UAV-based Virtual Outcrop Models (VOMs) of the slope(边坡), (ii) a geomechanical characterisation of the rock mass(大众) through the VOM interpretation(解释) and conventional(常规) field data(数据), and (iii) 3D Limit Equilibrium (LE) slope(边坡) stability(稳定性) analyses(分析). This study highlights the potential(潜在) of UAV surveys for providing valuable(宝贵) data(数据) for stability(稳定性) analyses(分析), especially in emergency(紧急) conditions such(这样) as in the aftermath of seismic events.

A continuous 18-10.2 ka paleo-earthquake events revealed by the Luobozhai lacustrine sediments, eastern Tibetan Plateau

To assess the regional seismicity during the late Quaternary, we studied a well−exposed sequence of Luobozhai lacustrine sediments, 14.5 m thick, near Lake Diexi in the upper reaches of the Minjiang River, eastern Tibetan Plateau. High-resolution geochemical, grain-size, and magnetic susceptibility indicators were then used to obtain a continuous record of the changes in elemental and physical properties within the profile to determine additional seismic events that did not result in soft sediment deformation structures. Rapidly deposited layers with underlying soft sediment deformation structures in the Luobozhai section indicate 18 siliciclastic-enriched sandy sediment fluxes from earthquake-triggered landslides or dust of siliciclastic-enriched clastic materials; isolated rapidly deposited layers (without underlying soft sediment deformation structures) record 17 additional earthquake-induced inputs of detrital material into the lake. Hence, there are a total of 35 seismic events (M ≥ 5.0) recorded within the Luobozhai section, which probably reflects the frequent seismicity of Minjiang fault. Optically stimulated luminescence and 14C dating of the Luobozhai section indicates continuous sediment deposition from 18 to 10.2 ka, and had a mean recurrence interval of approximately 224 years. Heavy mineral analysis shows that event layers and non-event layers have different mineral compositions. This reflects changes in sedimentation pre, during, and post a seismic event, especially the co-seismic debris material (e.g., from landslides and/or dust storms) that controls changes in provenance within the drainage basin, and in turn affects the regional erosion rate and weathering patterns. This study opens up new avenues of paleo-earthquake research.

Preliminary documentation of coseismic ground failure triggered by the February 6, 2023 Türkiye earthquake sequence

The devastating Kahramanmaraş earthquake sequence occurred on February 6, 2023. Two main events, Mw 7.8 and Mw 7.5 occurred 9 hours apart, affected 11 cities in Turkey, and subjected an area of ∼90,000 km2 to shaking levels known to trigger landslides (peak ground acceleration > 0.08 g). Extensive landsliding was expected given the hilly terrain affected by this significant ground shaking—about 15% of the topography is steeper than 20°—but was not initially apparent in early satellite imagery, mostly because of obscuring snow that fell just after the earthquakes. However, after a more detailed investigation using high-resolution satellite images, aerial photos, and a field survey, we confirmed that this earthquake sequence did, indeed, trigger numerous landslides. In this study, we present those findings and provide a preliminary characterization of the spatial distribution, general characteristics, and dominant types of landslides and hillslope deformation triggered by the earthquake sequence. We mapped 3673 coseismic landslides, mostly concentrated in the northern half of the impacted area. Rock falls are the most abundant landslide type, but bedrock rotational landslides, translational slides and lateral spreads are also numerous. Surface rupture through mountainous terrain caused several large, and in some cases fatal, landslides. Incipient landslides and ground cracks are also widespread, especially in the north. Lithology, spatial variability of ground shaking, and topographic relief appear to be the main variables controlling the spatial distribution of coseismic landslides. There are few detailed studies of earthquake-triggered landslides in arid and semi-arid regions such as this one, nor for such complex earthquake sequences. Therefore, this contribution provides valuable information for future hazard and modeling efforts in arid and semi-arid regions.

Earthquake Environmental Effects and Building Properties Controlling Damage Caused by the 6 February 2023 Earthquakes in East Anatolia

On 6 February 2023, East Anatolia was devastated by two major earthquakes resulting in hundreds of thousands of collapses and tens of thousands of human casualties. This paper investigates the factors related to building properties and earthquake environmental effects (EEEs) that contributed to the building damage grade and distribution in southeastern Turkey. In regards to the building construction properties, the loose enforcement of the building code, the random urban planning solutions and the poor construction standards are the main construction deficiencies that led to one of the largest disasters in Turkey’s recent history. Regarding geological factors, the triggering of primary and secondary EEEs largely shaped the grade and distribution of damage. Where coseismic surface ruptures intersected with the built environment, heavy to very heavy structural damage was observed. This was evident in many cases along the ruptured segments of the East Anatolian Fault Zone (EAFZ). Liquefaction observed close to waterbodies caused damage typical of building foundation load-bearing capacity loss. The earthquake-triggered landslides affected mainly mountainous and semi-mountainous settlements characterized with pre-earthquake high related susceptibility. The high susceptibility to generation of EEEs was extensively confirmed in many cases resulting in extensive damage. The provided information highlights the importance of such studies for hazard mitigation and disaster risk reduction.

Co-Seismic Landslides Triggered by the 2014 Mw 6.2 Ludian Earthquake, Yunnan, China: Spatial Distribution, Directional Effect, and Controlling Factors

The 2014 Mw 6.2 Ludian earthquake exhibited a structurally complex source rupture process and an unusual spatial distribution pattern of co-seismic landslides. In this study, we constructed a spatial database consisting of 1470 co-seismic landslides, each exceeding 500 m2. These landslides covered a total area of 8.43 km2 and were identified through a comprehensive interpretation of high-resolution satellite images taken before and after the earthquake. It is noteworthy that the co-seismic landslides do not exhibit a linear concentration along the seismogenic fault; instead, they predominantly extend along major river systems with an NE–SW trend. Moreover, the southwest-facing slopes have the highest landslide area ratio of 1.41. To evaluate the susceptibility of the Ludian earthquake-triggered landslides, we performed a random forest model that considered topographic factors (elevation, slope, aspect, distance to rivers), geological factors (lithology), and seismic factors (ground motion parameters, epicentral distance, distance to the seismogenic fault). Our analysis revealed that the distance to rivers and elevation were the primary factors influencing the spatial distribution of the Ludian earthquake-triggered landslides. When we considered the directional variation in ground motion parameters, the AUC of the model slightly decreased. However, incorporating this variation led to a significant reduction in the proportion of areas classified as “high” and “very high” landslide susceptibility. Moreover, SEDd emerged as the most effective ground motion parameter for interpreting the distribution of the co-seismic landslides when compared to PGAd, PGVd, and Iad.

Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China

Abstract. To enhance the timeliness and accuracy of spatial prediction of coseismic landslides, we propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software named Mat.LShazard V1.0. Based on this software, we evaluate the applicability of this improved spatial prediction strategy in six earthquake events that have occurred near the Sichuan–Yunnan region, including the Wenchuan, Ludian, Lushan, Jiuzhaigou, Minxian, and Yushu earthquakes. The results indicate that in the first stage (immediately after the quake event), except for the 2013 Minxian earthquake, the area under the curve (AUC) values of the modeling performance are above 0.8. Among them, the AUC value of the Wenchuan earthquake is the highest, reaching 0.947. The prediction results in the first stage can meet the requirements of emergency rescue by immediately obtaining the overall predicted information of the possible coseismic landslide locations in the quake-affected area. In the second and third stages, with the improvement of landslide data quality, the prediction ability of the model based on the entire landslide database is gradually improved. Based on the entire landslide database, the AUC value of the six events exceeds 0.9, indicating a very high prediction accuracy. For the second and third stages, the predicted landslide area (Ap) is relatively consistent with the observed landslide area (Ao). However, based on the incomplete landslide data in the meizoseismal area, Ap is much smaller than Ao. When the prediction model based on complete landslide data is built, Ap is nearly identical to Ao. This study provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages of emergency rescue, temporary resettlement, and late reconstruction after a major earthquake.

FREQUENCY RATIO ASSESSMENT FOR LANDSLIDES TRIGGERED BY 6 FEBRUARY 2023 KAHRAMANMARAS TURKIYE EARTHQUAKES BETWEEN GOLBASI AND ERKENEK

Abstract. Landslides triggered by earthquakes are significant geological hazards that can have devastating consequences, posing risks to human lives, infrastructure, and the environment. These seismic events may cause the instability of slopes and result in the displacement of soil and rock materials, leading to landslides. It is crucial to understand the characteristics and mechanisms of earthquake-triggered landslides in order to effectively manage and mitigate their associated risks. The number of landslides triggered by the 2023 Kahramanmaraş earthquakes (with magnitudes of 7.7 and 7.6) was over three thousand and their destructive effects were also devastating as secondary hazards. This study aims to examine the characteristics of landslides using the frequency ratio (FR) model. A landslide susceptibility map (LSM) was also produced using the output. For this purpose, in this study, we derived landslides triggered by the earthquakes in a part of the earthquake-affected region, between Golbasi town of Adiyaman and Erkenek village of Malatya covering an area with a size of 625 km2. The study utilized a landslide inventory that was manually delineated by visual interpretation based on pre-event and post-event. These associations can serve as a foundation for the application of various data-driven machine learning techniques. The findings of this study will contribute to the development of accurate LSMs, providing crucial insights into the behavior of earthquake-triggered landslides.

Insights into the mechanism of earthquake-triggered landslides in plastic fine-grained soils

This article seeks to investigate the cyclic behaviour of fine-grained soils and clarify the mechanism of earthquake-triggered landslides in this material. Six soils with different plasticity and mineralogy were tested in a series of undrained ring-shear experiments using different stress histories. The obtained results were analyzed in relation to the soil plasticity, clay mineralogy, and soil microfabrics to better understand the main factors leading to failure. It was found that the low-plasticity soil mixtures containing either kaolinite or illite were susceptible to liquefaction, while more plastic soil specimens that contained smectite were resistant to liquefaction. The mechanism of earthquake-triggered landslides and the factors affecting them were discussed, and it was experimentally shown that the failure in plastic fine-grained soils was not driven by the excess pore water pressure. Instead, it occurred mostly due to the large permanent shear displacements developed during cyclic loading.

Earthquake-triggered landslides and Environmental Seismic Intensity: insights from the 2018 Papua New Guinea earthquake (M w 7.5)

Earthquake-triggered landslides and Environmental Seismic Intensity: insights from the 2018 Papua New Guinea earthquake (M _w 7.5)

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.

A web-based GIS (web-GIS) database of the scientific articles on earthquake-triggered landslides

Abstract. Over the last 2 decades, the topic of earthquake-triggered landslides (EQTLs) has shown increasing relevance in the scientific community. This interest is confirmed by the numerous articles published in international, peer-reviewed journals. In this work we present a database containing a selection of articles published on this topic from 1984 to 2021. The articles were selected through a systematic search on the Clarivate™ Web of Science™ Core Collection online platform and were catalogued into a web-based GIS (web-GIS), which was specifically designed to show different types of information. After a general analysis of the database, for each article the following aspects were identified: the bibliometric information (e.g. author(s), title, publication year), the relevant topic and sub-topic category (or categories), and the earthquake(s) addressed. The analysis allowed us to infer general information and statistics on EQTLs (e.g. relevant methodological approaches over time and in relation to the scale of investigation, most studied events), which can be useful to obtain a spatial distribution of the articles and a general overview of the topic.

Large-deformation modelling of earthquake-triggered landslides considering non-uniform soils with a stratigraphic dip

Natural soil slopes often exhibit both spatial variability and stratigraphic dips due to complex depositional processes of soil, exerting a remarkable influence on landslide behaviors. Existing research mostly focused on the influence of soil spatial variability on slope stability or landslide runout behaviors, and the investigation of the effect of stratigraphic dip on large-deformation landslide consequences are relatively limited and have not been fully understood. This study therefore proposed a random large-deformation finite-element (LDFE) computation framework, combining the coupled Eulerian-Lagrangian technique and a three-dimensional (3D) rotational random field to insight the earthquake-triggered landslide consequences considering non-uniform soils with a stratigraphic dip. The whole runout process of earthquake-triggered landslides from activation to termination is realistically reproduced. By implementing both two-dimension (2D) and 3D random LDFE analyses coupled with Monte-Carlo simulations, both effects of spatial non-uniformity and stratigraphic dip of soil on the evolution process and consequences of landslides are analyzed in detail. The results reveal that the stratigraphic dip greatly influences landslide behaviors. In addition, the 2D analyses probably underestimate the landslide consequences when contrasting with 3D analyses. These findings highlight the importance of considering stratigraphic dip of soil slope and the advantage of 3D random LDFE analysis on landslide behaviors.

Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network

Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network

How Spatial Resolution of Remote Sensing Image Affects Earthquake Triggered Landslide Detection: An Example from 2022 Luding Earthquake, Sichuan, China

The magnitude 6.8 Luding earthquake that occurred on 5 September 2022, triggered multiple large-scale landslides and caused a heavy loss of life and property. The investigation of earthquake-triggered landslides (ETLs) facilitates earthquake disaster assessments, rescue, reconstruction, and other post-disaster recovery efforts. Therefore, it is important to obtain landslide inventories in a timely manner. At present, landslide detection is mainly conducted manually, which is time-consuming and laborious, while a machine-assisted approach helps improve the efficiency and accuracy of landslide detection. This study uses a fully convolutional neural network algorithm with the Adam optimizer to automatically interpret the aerial and satellite data of landslides. However, due to the different resolutions of the remote sensing images, the detected landslides vary in boundary and quantity. In this study, we conducted an assessment in the study area of Wandong village in the earthquake-affected area of Luding. UAV images, GF-6 satellite images, and Landsat 8 satellite images, with a resolution of 0.2 m, 2 m, and 15 m, respectively, were selected to detect ETLs. Then, the accuracy of the results was compared and verified with visual detection results and field survey data. The study indicates that as the resolution decreases, the accuracy of landslide detection also decreases. The overall landslide area detection rate of UAV imagery can reach 82.17%, while that of GF-6 and Landsat 8 imagery is only 52.26% and 48.71%. The landslide quantity detection rate of UAV imagery can reach 99.07%, while that of GF-6 and Landsat 8 images is only 48.71% and 61.05%. In addition, for each landslide detected, little difference is found in large-scale landslides, and it becomes more difficult to correctly detect small-scale landslides as the resolution decreases. For example, landslides under 100 m2 could not be detected from a Landsat 8 satellite image.

Earthquake-Induced Tsunamis in Western Greece (Ionian Sea and Western and Southern Peloponnese): Use of Tsunami Quantities, Impact and ITIS-2012 Intensities for Highlighting Susceptible Areas

Taking into account recent studies on the tsunamigenic potential of strike-slip faults, it is concluded that there is a need to reassess their near-source tsunami hazard and risk. One of the areas which needs reassessment is Western Greece, especially the Ionian Islands and the western coastal Peloponnese, where major seismogenic strike-slip structures occur. In this context, an extensive review of the available literature is conducted, including not only earthquake and tsunami catalogues but also tsunamis’ imprints on the stratigraphic record. It is concluded that the Ionian Islands and the western Peloponnese have a rich history of tsunamis since 6000 BC, revealing that they are subjected to high tsunami hazard. In addition to the teletsunami effects of distant earthquakes, there are also local tsunamis with smaller physical quantities and slighter coastal impact that are attributed mainly to local offshore faults and earthquake-triggered landslides. The fact that no destructive local tsunamis have been detected so far does not exclude the possibility of future triggering. In order to identify areas susceptible to future tsunami impact, we extract tsunami quantities and coastal impact data from available sources and we apply the Integrated Tsunami Intensity Scale 2012 (ITIS-2012) for all the events with available and adequate information. The highly susceptible areas comprise straits, funnel-shaped bays and extensive coastal areas exposed to major strike-slip seismogenic sources in the Ionian Sea and the western Hellenic Trench. Based on the aforementioned information, the inclusion of the Ionian Sea in the tsunamigenic zones of Greece is strongly recommended.

Assessment of earthquake-triggered landslide susceptibility considering coseismic ground deformation

The distance to the surface rupture zone has been commonly regarded as an important influencing factor in the evaluation of earthquake-triggered landslide susceptibility. However, the obvious surface rupture zones usually do not occur in some buried-fault earthquake cases, which means information about the distance to the surface rupture is lacking. In this study, a new influencing factor named coseismic ground deformation was added to remedy this shortcoming. The Mid-Niigata prefecture earthquake was regarded as the study case. To select a more suitable model for generating the landslide susceptibility map, three commonly used models named logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM) were also conducted to assess landslide susceptibility. The performances of these three models were evaluated with the receiver operating characteristic curve. The calculated results showed that the ANN model has the highest area under the curve (AUC) value of 0.82. As the earthquake triggered more landslides in the epicenter area, which makes it more prone to landslides in further earthquakes, the susceptibility analysis at two different mapping scales (the whole study area and the epicenter area) was also applied.