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Inventories of seismically induced landslides provide essential information about the extent and severity of ground effects after an earthquake. Rigorous assessment of the completeness of a landslide inventory and the quality of a landslide susceptibility map derived from the inventory is of paramount importance for disaster management applications. Methods and materials applied while preparing inventories influence their quality, but the criteria for generating an inventory are not standardized. This study considered five landslide inventories prepared by different authors after the 2015 Gorkha earthquake, to assess their differences, understand the implications of their use in producing landslide susceptibility maps in conjunction with standard landslide predisposing factors and logistic regression. We adopted three assessment criteria: (1) an error index to identify the mutual mismatches between the inventories; (2) statistical analysis, to study the inconsistency in predisposing factors and performance of susceptibility maps; and (3) geospatial analysis, to assess differences between the inventories and the corresponding susceptibility maps. Results show that substantial discrepancies exist among the mapped landslides. Although there is no distinct variation in the significance of landslide causative factors and the performance of susceptibility maps, a hot spot analysis and cluster/outlier analysis of the maps revealed notable differences in spatial patterns. The percentages of landslide-prone hot spots and clustered areas are directly proportional to the size of the landslide inventory. The proposed geospatial approaches provide a new perspective to the investigators for the quantitative analysis of earthquake-triggered landslide inventories and susceptibility maps.

In this work, we present a novel quantitative geographical information system-based procedure to obtain the magnitude (area) and frequency of medium to large first-time shallow slope failures. The procedure has been set up at the Barcedana Valley, in the Tremp Basin (Eastern Pyrenees). First, pixel-based susceptibility classes were defined using a slope stability index obtained with the physically based model SINMAP. The frequency calculated from the number of first-time failures recorded during the last 60 years was then assigned to each susceptibility class. We devised a procedure to estimate the size of potential failures by means of the aggregation of pixels within the boundaries of morphological slope units, optimized for the purpose. Finally, the landslide hazard was prepared using the magnitude-frequency matrix. Results show that a proper pixel clustering has been carried which avoids the generation of small groups of pixels with different susceptibility degrees within the same slope unit. For a given hill slope, the area of the cluster of pixels depends on the size of the slope unit, which is not unique as it depends on the criterion used to delineate them. Therefore, the latter is a key factor in the final results. In this study, we validated our results with the size distribution of the observed landslides. The methodology presented in this work can be applied using any susceptibility assessment method with a pixel-based output.

One of the main challenges hampering an accurate measurement of the double parton scattering (DPS) cross sections is the difficulty in separating the DPS from the leading twist (LT) contributions. We argue that such a separation can be achieved, and cross section of DPS measured, in proton–nucleus scattering by exploiting the different centrality dependence of DPS and LT processes. We developed a Monte Carlo implementation of the DPS processes which includes realistic nucleon–nucleon (NN) correlations in nuclei, an accurate description of transverse geometry of both hard and soft NN collisions as well as fluctuations of the strength of interaction of nucleon with nucleus (color fluctuation effects). Our method allows the calculation of probability distributions of single and double dijet events as a function of centrality, also distinguishing double hard scatterings originating from a single target nucleon and from two different nucleons. We present numerical results for the rate of DPS as a function of centrality, following the model developed by the ATLAS collaboration which relates the distribution over the number of wounded nucleons to the distribution over the sum of transverse energies of hadrons produced at large negative (along the nucleus direction) rapidities, which is experimentally measurable. We suggest a new quantity which allows to test the geometry of DPS and we argue that it is a universal function of centrality for different DPS processes. This quantity can be tested by analyzing existing LHC data. The method developed in this work can be extended to the search for triple parton interactions.

A landslide event is characterized by the distribution of landslides caused by a single triggering event. The severity of earthquake-induced landslide events can be quantified by the landslide-event magnitude, a metric derived from the frequency-size distribution of landslide inventories. However, reliable landslide inventories are not available for all earthquakes, because the preparation of a suitable inventory requires data, time, and expertise. Prediction of landslide-event magnitude immediately following an earthquake provides an estimate of the total landslide area and volume based on empirical relations. It allows to make an assessment of the severity of a landslide event in near-real time and to estimate the frequency-size distribution curve of the landslides. In this study, we used 23 earthquake-induced landslide inventories and propose a method to predict landslide-event magnitude. We selected five predictors, both morphometric and seismogenic, which are globally and readily available. We used the predictors within a stepwise linear regression and validated using the leave-one-out technique. We show that our approach successfully predicts landslide-event magnitude values and provides results along with their statistical significance and confidence levels. However, to test the validity of the approach globally, it should be calibrated using a larger and more representative dataset. A global, near real-time assessments regarding landslide-event magnitude scale can then be achieved by retrieving the readily available ShakeMaps, along with topographic and thematic information, and applying the calibrated model. The results may provide valuable information regarding landscape evolution processes, landslide hazard assessments, and contribute to the rapid emergency response after earthquakes in mountainous terrain.

Geomorphological field mapping is a conventional method used to prepare landslide inventories. The approach is typically hampered by the accessibility and visibility, during field campaigns for landslide mapping, of the different portions of the study area. Statistical significance of landslide susceptibility maps can be significantly reduced if the classification algorithm is trained in unsurveyed regions of the study area, for which landslide absence is typically assumed, while ignorance about landslide presence should actually be acknowledged. We compare different landslide susceptibility zonations obtained by training the classification model either in the entire study area or in the only portion of the area that was actually surveyed, which we name effective surveyed area. The latter was delineated by an automatic procedure specifically devised for the purpose, which uses information gathered during surveys, along with landslide locations. The method was tested in Gipuzkoa Province (Basque Country), north of the Iberian Peninsula, where digital thematic maps were available and a landslide survey was performed. We prepared the landslide susceptibility maps and the associated uncertainty within a logistic regression model, using both slope units and regular grid cells as the reference mapping unit. Results indicate that the use of effective surveyed area for landslide susceptibility zonation is a valid approach that minimises the limitations stemming from unsurveyed regions at landslide mapping time. Use of slope units as mapping units, instead of grid cells, mitigates the uncertainties introduced by training the automatic classifier within the entire study area. Our method pertains to data preparation and, as such, the relevance of our conclusions is not limited to the logistic regression but are valid for virtually all the existing multivariate landslide susceptibility models.

Slope units represent surface slopes by means of polygons delimited by drainage and divide lines obtained on a digital topography. Objective slope unit delineation for a given digital elevation model is still an open issue and, often, a limitation that may dictate the use of a more traditional pixel-based approach for spatial analysis. Availability of slope unit maps facilitates many kinds of studies and allows scholars to focus on specific scientific issues rather than on preparing sound mapping units from scratch for their research. Here, we present a slope unit map of a large portion of the Himalayas. The map is prepared following a widely tested, parameter-free optimization algorithm. The area encompassed by the map is relevant to studies of the well-known 2015 Gorkha earthquake and monsoons, which makes it relevant to a vast portion of the scientific community working in natural hazards including, but not limited to, landslide scientists and practitioners. The map contains 112,674 polygons with average area of 0.38 km and is published in vector form. The map is accompanied by a selection of data including morphometric and thematic quantities. In addition to describing the rationale behind the delineation of the polygonal map and selected data, we describe an application devoted to unsupervised terrain classification. We applied a k-means clustering procedure with two strategies: one at (coarser) basin scale and one at (finer) slope unit scale. We show similarities and differences between the two classification strategies, highlighting the role of the slope unit subdivision in the two cases.

Ischia Island is a volcano-tectonic horst in the Phlegrean Volcanic District, Italy. We investigated rockfalls in Ischia using STONE, a three-dimensional model for simulating trajectories for given detachment locations of blocks. We propose methodological advances regarding the use of high-resolution LiDAR elevation data, the localization of possible detachments sources, and the inclusion of scenario-based seismic shaking as a trigger for rockfalls. We demonstrated that raw LiDAR data are useful to distinguish areas covered by tall vegetation, allowing realistic simulation of trajectories. We found that the areas most susceptibile to rockfalls are located along the N, N-W and S-W steep flanks of Mt. Epomeo, the S and S-W coast, and the sides of some steep exposed hydrographic channels located in the southern sector of the island. A novel procedure for dynamic activation of sources depending on ground shaking, in the event of an earthquake, helped inferring a seismically-triggered source map and the corresponding rockfall trajectories, for a scenario with 475 y return time. Thus, we obtained preliminary rockfall suceptibility in Ischia both in a \"static\" (trigger-independent) scenario, and in a seismic shaking triggering scenario. They must not be considered a risk map, but a starting point for a detailed field analysis.

Starting on 24th August 2016, Central Italy was struck by a six-month earthquake sequence that caused 303 victims and extensive major damages to urban areas and infrastructures, in some cases entire villages needed complete rebuilding. In this paper we present a map that portrays the overall susceptibility to multiple landslide types and the exposure to landslides of the rural-urban areas of the Castelsantangelo sul Nera Municipality, a typical village of the central Italian Apennine. The map is based on a procedure that ingests geomorphological data and models and groups the individual landslide susceptibility maps in a joint susceptibility and exposure map based on expert-defined criteria. The procedure has been applied to built-up and to undeveloped areas to highlight their exposure and was used as a tool for planning post-seismic reconstruction. We advise that such maps are used also as basic tool for ordinary urban planning.

Slope failures pose a substantial threat to mining activity due to their destructive potential and high probability of occurrence on steep slopes close to limit equilibrium conditions, which are often found both in open pits and in waste and tailing disposal facilities. The development of slope monitoring and modeling programs usually entails the exploitation of in situ and remote sensing data, together with the application of numerical modeling, and it plays an important role in the definition of prevention and mitigation measures aimed at minimizing the impact of slope failures in mining areas. In this paper, a new methodology is presented; one that combines satellite radar interferometry and 2D finite element modeling for slope stability analysis at a regional scale, and applied within slope unit polygons. Although the literature includes many studies applying radar interferometry and modeling for slope stability analysis, the addition of slope units as input data for radar interferometry and modeling purposes has, to our knowledge, not previously been reported. A former mining area in southeast Spain was studied, and the method proved useful for detecting and characterizing a large number of unstable slopes. Out of the 1959 slope units used for the spatial analysis of the radar interferometry data, 43 were unstable, with varying values of safety factor and landslide size. Out of the 43 active slope units, 21 exhibited line of sight velocities greater than the maximum error obtained through validation analysis (2.5 cm/year). Finally, this work discusses the possibility of using the results of the proposed approach to devise a proxy for landslide hazard. The proposed methodology can help to provide non-expert final users with intelligible, clear, and easily comparable information to analyze slope instabilities in different settings, and not limited to mining areas.

The island of Gran Canaria (Canary Islands, Spain) is characterized by a large variability of volcanic rocks reflecting its volcanic evolution. The geological map provided by Geological Survey of Spain at 1:25.000 scale shows more than 109 different lithologies and it is too complex for environmental and engineering purposes. This work presents a simplified geotechnical map with a small number of classes grouping up units with similar geotechnical behaviours. The lithologies were grouped using about 350 rock samples, collected in the seven major islands of the Archipelago. The geotechnical map was used to model rockfall hazard in the entire island of Gran Canaria, where rockfalls are an important threat. The rockfall map was validated with 128 rockfall events along the GC-200 road, located in the NW sector of Gran Canaria. About 96% of the events occurred along sections of the road where the number of expected trajectories is high or moderate.