An Open Repository of Earthquake-Triggered Ground-Failure Inventories

Schmitt, Robert G.; Tanyaş, Hakan; Jessee, M. Anna Nowicki; Zhu, Jianfeng; Biegel, Katherine M.; Allstadt, Kate E.; Jibson, Randall W.; Westen, C.J. van; Sato, Hiroshi; Wald, David J.; Godt, Jonathan W.; Görüm, Tolga; Xu, Chang; Rathje, Ellen M.; Knudsen, Jørgen Kjetil

doi:10.5066/f7h70db4

Cited by 19 publications

(14 citation statements)

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“…Central to our analysis are the location and size of mapped landslides triggered by individual earthquakes. Currently, we have access to landslide data from 36 EQIL inventories, a subset of those summarized by Tanyas et al (2017; see Table 1 and Figure 1). Twenty-one of these inventories are now publicly available in Schmitt et al (2017).…”

Section: Landslide Data (Dependent Variable)mentioning

confidence: 99%

“…Currently, we have access to landslide data from 36 EQIL inventories, a subset of those summarized by Tanyas et al (2017; see Table 1 and Figure 1). Twenty-one of these inventories are now publicly available in Schmitt et al (2017). This data set includes landslides mapped by a variety of methods including fieldbased mapping of observed landslide deposits, and remote sensing techniques.…”

Section: Landslide Data (Dependent Variable)mentioning

confidence: 99%

“…In order to use a majority of these inventories while reducing the amount of low-quality data in our global dataset, we follow the scoring technique described by Tanyas et al (2017). For this model, we utilize their essential criteria required for inventories to be used for hazard assessment; these include whether (1) the study area was systematically analyzed by visual interpretation using satellite imagery (as compared to landslides mapped using automatic image classification methods; 0-100% value), (2) a boundary of the mapping area was indicated (no/yes as 0 or 1), (3) landslides that occurred before and after the earthquake were removed from the inventory (0-100% value), and (4) the landslides were mapped at a high enough resolution to recognize individual landslides (0-1 value based on the smallest landslides that were mapped in that inventory).…”

Section: Landslide Data (Dependent Variable)mentioning

confidence: 99%

“…This research was supported by two USGS NEHRP grants: G14AP00047 and G17AP00017. Data used in this analysis include mapped landslide inventories (Tanyas et al, 2017); a subset of these landslide inventories is publicly available in an open access data repository from Schmitt et al (2017). Mean and median elevation data at 7.5c resolution from GMTED topography data (Danielson & Gesch, 2011).…”

Section: Acknowledgmentsmentioning

confidence: 99%

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

et al. 2018

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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 data set 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 that 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 versus 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.

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Section: Landslide Data (Dependent Variable)mentioning

confidence: 99%

Section: Landslide Data (Dependent Variable)mentioning

confidence: 99%

Section: Landslide Data (Dependent Variable)mentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

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

et al. 2018

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“…This highlights the importance of continued collaboration with the research community to develop new modeling approaches. To better facilitate such research, we maintain a public repository of inventories contributed by many authors (Allstadt and Schmitt, 2017; Schmitt et al, 2017) to ease access to the key input data required for model development. Similarly, the USGS maintains a database of curated ShakeMaps; the newest release is the ShakeMap Atlas 4 (Marano et al, 2021), which includes all events for which GF inventories are available in the USGS inventory repository.…”

Section: Discussionmentioning

confidence: 99%

The US Geological Survey ground failure product: Near-real-time estimates of earthquake-triggered landslides and liquefaction

et al. 2021

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Since late 2018, the US Geological Survey (USGS) ground failure (GF) earthquake product has provided publicly available spatial estimates of earthquake-triggered landslide and liquefaction hazards, along with the qualitative hazard and population exposure-based alerts for M > 6 earthquakes worldwide and in near real time (within ∼30 min). Earthquake losses are oftentimes greatly aggravated by the impacts due to ground failure, yet those particular events with dramatic additional losses have not, heretofore, been rapidly identifiable. The GF product now provides situational awareness about the potential extent and severity of ground failure in the crucial time period before direct observations are available. We describe our implementation of the GF product and the lessons learned from the earthquakes that have occurred since the GF product was released. We describe the product design process, the underlying GF models, the methods we have developed for modeling uncertainty, and the development of the alert levels. The GF product has been produced in near real time for 320 events over the 2-year period since its public implementation in late 2018 through early 2021. The majority of these events yielded the lowest level (green) alerts for all ground-failure types, with 25 resulting in elevated hazard or exposure to landslides and 47 for liquefaction. In a qualitative comparison between the GF product alerts and GF occurrence information, we found that the product succeeds at assigning appropriate alert levels in the majority of cases. Based on our experience with the product, we have identified the following priorities for future improvements: (1) refinements of the underlying probabilistic models to incorporate severity and explicitly model the type of landslide/liquefaction; (2) development of models for fatalities and economic losses due to ground failure; and (3) estimation of the impacts of ground failure on infrastructure.

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Presentation and Analysis of a Worldwide Database of Earthquake‐Induced Landslide Inventories

et al. 2017

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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.

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An Open Repository of Earthquake-Triggered Ground-Failure Inventories

Cited by 19 publications

References 0 publications

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

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

The US Geological Survey ground failure product: Near-real-time estimates of earthquake-triggered landslides and liquefaction

Presentation and Analysis of a Worldwide Database of Earthquake‐Induced Landslide Inventories

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