Hakan Tanyas

Presentation and Analysis of a Worldwide Database of Earthquake-Induced Landslide Inventories : Earthquake-Induced Landslide Inventories

Earthquake-induced landslide (EQIL) inventories are essential tools to extend our knowledge of the relationship between earthquakes and the landslides they can trigger. Regrettably, such inventories are difficult to generate and therefore scarce, and the available ones differ in terms of their quality and level of completeness. Moreover, access to existing EQIL inventories is currently difficult because there is no centralized database. To address these issues, we compiled EQIL inventories from around the globe based on an extensive literature study. The database contains information on 363 landslide-triggering earthquakes and includes 66 digital landslide inventories. To make these data openly available, we created a repository to host the digital inventories that we have permission to redistribute through the U.S. Geological Survey ScienceBase platform. It can grow over time as more authors contribute their inventories. We analyze the distribution of EQIL events by time period and location, more specifically breaking down the distribution by continent, country and mountain region. Additionally, we analyze frequency distributions of EQIL characteristics, such as the approximate area affected by landslides, total number of landslides, maximum distance from fault rupture zone, and distance from epicenter when the fault plane location is unknown. For the available digital EQIL inventories, we examine the underlying characteristics of landslide size, topographic slope, roughness, local relief, distance to streams, peak ground acceleration, peak ground velocity, and Modified Mercalli Intensity. Also, we present an evaluation system to help users assess the suitability of the available inventories for different types of EQIL studies and model development.

A Global Empirical Model for Near‐Real‐Time Assessment of Seismically Induced Landslides

Earthquake‐triggered landslides are a significant hazard in seismically active regions, but our ability to assess the hazard they pose in near real‐time is limited. In this study, we present a new globally applicable model for seismically induced landslides based on the most comprehensive global dataset available; we use 23 landslide inventories that span a range of earthquake magnitudes and climatic and tectonic settings. We use logistic regression to relate the presence and distribution of earthquake‐triggered landslides with spatially distributed estimates of ground shaking, topographic slope, lithology, land cover type, and a topographic index designed to estimate variability in soil wetness to provide an empirical model of landslide distribution. We tested over 100 combinations of independent predictor variables to find the best‐fitting model, using a diverse set of statistical tests. Blind validation tests show the model accurately estimates the distribution of available landslide inventories. The results indicate that the model is reliable and stable, with high “balanced accuracy” (correctly vs. incorrectly classified pixels) for the majority of test events. A cross validation analysis shows high balanced accuracy for a majority of events as well. By combining near‐real time estimates of ground shaking with globally available landslide susceptibility data, this model provides a tool to estimate the distribution of coseismic landslide hazard within minutes of the occurrence of any earthquake worldwide for which a U.S. Geological Survey ShakeMap is available.

Identification of possible source areas of stone raw materials combining remote sensing and petrography

ABSTRACT Understanding the location and distribution of raw materials used in the production of prehistoric artefacts is a significant part of archaeological research that aims to understand the interregional interaction patterns in the past. The aim of this study is to explore the regional locations of the source rock utilized in the production of stone bowls, which were unearthed at the Neolithic (approximately 6500–5500 BC) site of Domuztepe (Kahramanmaraş-Turkey), via a combination of remote-sensing methods, petrographic and chemical analyses. To accomplish this task, the stone bowls were identified mineralogically, geochemically and spectrally, and then mapped with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensors. According to the defined mineralogical composition, which is iron-rich chlorite, the target areas were selected among geologically potential areas that would bear similar source rocks in near vicinity and the target spectral signature was searched within these target areas. In order to overcome the problem of spectral similarity of chlorite group to some other minerals such as carbonate or epidote group minerals, band ratioing (BR) and feature-oriented principal component analysis (FOPCA) were used with an integrated approach and then their results were filtered according to the outcomes of the relative absorption band-depth (RBD) images. The areas with highest potentials were vectorized and then field checked. Mineralogical investigations on the collected field samples reveal that there is a mineralogical match between the source and target material. One group of stone bowls samples have similar geochemical signatures as the field samples having ultramafic origins. However, there is another group of stone bowls samples which are geochemically dissimilar to the first group of field and bowls samples. The data regarding the geochemical signatures of these two groups indicate a genetic relation between the sample sets. Therefore, it is concluded that the source rock of a major portion of the stone bowls unearthed at Domuztepe most probably originated from the near vicinity of the site.

How size and trigger matter: analyzing rainfall- and earthquake-triggered landslide inventories and their causal relation in the Koshi River basin, Central Himalaya

Inventories of landslides caused by different triggering mechanisms, such as earthquakes, extreme rainfall events or anthropogenic activities, may show different characteristics in terms of distribution, contributing factors and frequency–area relationships. The aim of this research is to study such differences in landslide inventories and the effect they have on landslide susceptibility assessment. The study area is the watershed of the transboundary Koshi River in the central Himalaya, shared by China, Nepal and India. Detailed landslide inventories were generated based on visual interpretation of remote-sensing images and field investigation for different time periods and triggering mechanisms. Maps and images from the period 1992 to 2015 were used to map 5858 rainfall-triggered landslides, and after the 2015 Gorkha earthquake, an additional 14 127 coseismic landslides were mapped. A set of topographic, geological and land cover factors were employed to analyze their correlation with different types and sizes of landslides. The frequency–area distributions of rainfalland earthquake-triggered landslides (ETLs) have a similar cutoff value and power-law exponent, although the ETLs might have a larger frequency of a smaller one. In addition, topographic factors varied considerably for the two triggering events, with both altitude and slope angle showing significantly different patterns for rainfall-triggered and earthquake-triggered landslides. Landslides were classified into two size groups, in combination with the main triggering mechanism (rainfallor earthquake-triggered). Susceptibility maps for different combinations of landslide size and triggering mechanism were generated using logistic regression analysis. The different triggers and sizes of landslide data were used to validate the models. The results showed that susceptible areas for smalland large-size rainfalland earthquake-triggered landslides differed substantially.

An updated method for estimating landslide‐event magnitude

Summary statistics derived from the frequency–area distribution (FAD) of inventories of triggered landslides allows for direct comparison of landslides triggered by one event (e.g. earthquake, rainstorm) with another. Such comparisons are vital to understand links between the landslide‐event and the environmental characteristics of the area affected. This could lead to methods for rapid estimation of landslide‐event magnitude, which in turn could lead to estimates of the total triggered landslide area. Previous studies proposed that the FAD of landslides follows an inverse power‐law, which provides the basis to model the size distribution of landslides and to estimate landslide‐event magnitude (mLS), which quantifies the severity of the event. In this study, we use a much larger collection of earthquake‐induced landslide (EQIL) inventories (n=45) than previous studies to show that size distributions are much more variable than previously assumed. We present an updated model and propose a method for estimating mLS and its uncertainty that better fits the observations and is more reproducible, robust, and consistent than existing methods. We validate our model by computing mLS for all of the inventories in our dataset and comparing that with the total landslide areas of the inventories. We show that our method is able to estimate the total landslide area of the events in this larger inventory dataset more successfully than the existing methods.