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After large damaging earthquakes, fast and well-informed help efforts can save lives. The key information are the location and extent of damage and losses, in particular for disasters that affect large areas. Space-borne imaging methods, together with building-wise exposure data, can enable rapid damage assessments, delivering damage proxy maps for an entire affected region within hours. Space-borne damage proxy maps are generated e.g. from repeated synthetic aperture radar (SAR) imaging of the ground before and after an event to detect changes in the radar backscatter. Such changes are expressed in a drop of interferometric (InSAR) coherence. Dense human infrastructure, like building agglomerations, shows long-term stable backscatter characteristics and high coherence compared to many natural surfaces. Therefore, a sudden drop in coherence between pre- and post-earthquake images can be attributed to damage on the ground. However, without further information a detected damage tells little about the involved severity concerning human lives. Also, less dense settlement structures like villages may not show as long-term coherent areas and may not be included in damage maps even if strongly affected. Based on InSAR damage proxy maps from the 2023 Turkey Syria earthquakes, we present an approach to augment InSAR damage maps with information from exposure models combined with open building data. This combination can improve guiding rescue efforts by highlighting approximated damage with exposure models. Also, the building data help revealing blind spots in damage proxy maps, which are method-related, and should not go unnoticed., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

The Collaboratory for the Study of Earthquake Predictability (CSEP) is an open and global community whose mission is to accelerate earthquake predictability research through rigorous testing of probabilistic earthquake forecast models and prediction algorithms. pyCSEP supports this mission by providing open-source implementations of useful tools for evaluating earthquake forecasts. pyCSEP is a Python package that contains the following modules: (1) earthquake catalog access and processing, (2) representations of probabilistic earthquake forecasts, (3) statistical tests for evaluating earthquake forecasts, and (4) visualization routines and various other utilities. Most significantly, pyCSEP contains several statistical tests needed to evaluate earthquake forecasts, which can be forecasts expressed as expected earthquake rates in space–magnitude bins or specified as large sets of simulated catalogs (which includes candidate models for governmental operational earthquake forecasting). To showcase how pyCSEP can be used to evaluate earthquake forecasts, we have provided a reproducibility package that contains all the components required to re-create the figures published in this article. We recommend that interested readers work through the reproducibility package alongside this article. By providing useful tools to earthquake forecast modelers and facilitating an open-source software community, we hope to broaden the impact of the CSEP and further promote earthquake forecasting research.

Assessing post-seismic damage on an urban/regional scale remains relatively difficult owing to the significant amount of time and resources required to acquire informa- tion and conduct a building-by-building seismic damage assessment. However, the application of new methods based on artificial intelligence, combined with the increasingly systematic availability of field surveys of post-seismic damage, has pro- vided new perspectives for urban/regional seismic damage assessment. This study analyzes the effectiveness and relevance of a number of machine learning techniques for analyzing spatially distributed seismic damage after an earthquake at the regional scale. The basic structural parameters of a portfolio of buildings and the post- earthquake damage surveyed after the Nepal 2015 earthquake are analyzed and com- bined with macro-seismic intensity values provided by the United States Geological Survey ShakeMap tool. Among the methods considered, the random forest regres- sion model provides the best damage predictions for specified ground motion inten- sity values and structural parameters. For traffic-light-based damage classification (three classes: green-, amber-, and red-tagged buildings based on post-earthquake damage grade), a mean accuracy of 0.68 is obtained. This study shows that restricting learning to basic features of buildings (i.e. number of stories, height, plinth area, and age), which could be readily available from authoritative databases (e.g. national cen- sus) or field-surveyed databases, yields a reliable prediction of building damage (4 fea- tures/3 damage grade accuracy: 0.64).

Building exposure, that is, characterization of buildings number of people occupying it and its replacement cost, for a given region or area, is one of the primary components when determining building risk due to a hazard or group of hazards.We have developed an exposure model for Japan at the municipality level. This model is entirely derived from open building data (e.g., census) and resulting in an open inventory of uniformly defined building parameters available for risk assessments. The Japanese land and housing database (ESTAT: https://www.e-stat.go.jp/en) provides information about building types and their number, dwelling type, their number and size, tenure type, construction material type, year of construction, number of stories, persons per dwelling and size of dwelling (area in square meters). Different combinations of building parameters were mapped to building classes based on the taxonomy of the Global Earthquake Model (GEM).Quality of the model is greatly affected by the availability of building parameters and their survey quality in the census data , thus limiting the flexibility and versatility of the model to be up- or down-scaled.Despite the limitations, the model gives a very clear snapshot of distribution of regional building classes and their possible count. Also representing an open catalogue of rules for mapping Japanese building attributes and, in doing so, creates an opportunity for the scientific and non-scientific community to collaborate. We welcome corrections and expansion of individual aspects of the aggregated exposure model and like to encourage the development of such open aggregated exposure models for other countries., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

For government officials and the public to act on real-time forecasts of earthquakes, the seismological community needs to develop confidence in the underlying scientific hypotheses of the forecast generating models by assessing their predictive skill. For this purpose, the Collaboratory for the Study of Earthquake Predictability (CSEP) provides cyberinfrastructure and computational tools to evaluate earthquake forecasts. Here, we introduce pyCSEP, a Python package to help earthquake forecast developers embed model evaluation into the model development process. The package contains the following modules: (1) earthquake catalog access and processing, (2) data models for earthquake forecasts, (3) statistical tests for evaluating earthquake forecasts, and (4) visualization routines. pyCSEP can evaluate earthquake forecasts expressed as expected rates in space-magnitude bins, and simulation-based forecasts that produce thousands of synthetic seismicity catalogs. Most importantly, pyCSEP contains community-endorsed implementations of statistical tests to evaluate earthquake forecasts, and provides well defined file formats and standards to facilitate model comparisons. The toolkit will facilitate integrating new forecasting models into testing centers, which evaluate forecast models and prediction algorithms in an automated, prospective and independent manner, forming a critical step towards reliable operational earthquake forecasting.

For government officials and the public to act on real-time forecasts of earthquakes, the seismological community needs to develop confidence in the underlying scientific hypotheses of the forecast generating models by assessing their predictive skill. For this purpose, the Collaboratory for the Study of Earthquake Predictability (CSEP) provides cyberinfrastructure and computational tools to evaluate earthquake forecasts. Here, we introduce pyCSEP, a Python package to help earthquake forecast developers embed model evaluation into the model development process. The package contains the following modules: (1) earthquake catalog access and processing, (2) data models for earthquake forecasts, (3) statistical tests for evaluating earthquake forecasts, and (4) visualization routines. pyCSEP can evaluate earthquake forecasts expressed as expected rates in space-magnitude bins, and simulation-based forecasts that produce thousands of synthetic seismicity catalogs. Most importantly, pyCSEP contains community-endorsed implementations of statistical tests to evaluate earthquake forecasts, and provides well defined file formats and standards to facilitate model comparisons. The toolkit will facilitate integrating new forecasting models into testing centers, which evaluate forecast models and prediction algorithms in an automated, prospective and independent manner, forming a critical step towards reliable operational earthquake forecasting.

The creation of building exposure models for seismic risk assessment is frequently challenging due to the lack of availability of detailed information on building structures. Different strategies have been developed in recent years to overcome this, including the use of census data, remote sensing imagery and volunteered graphic information (VGI). This paper presents the development of a building-by-building exposure model based exclusively on openly available datasets, including both VGI and census statistics, which are defined at different levels of spatial resolution and for different moments in time. The initial model stemming purely from building-level data is enriched with statistics aggregated at the neighbourhood and city level by means of a Monte Carlo simulation that enables the generation of full realisations of damage estimates when using the exposure model in the context of an earthquake scenario calculation. Though applicable to any other region of interest where analogous datasets are available, the workflow and approach followed are explained by focusing on the case of the German city of Cologne, for which a scenario earthquake is defined and the potential damage is calculated. The resulting exposure model and damage estimates are presented, and it is shown that the latter are broadly consistent with damage data from the 1978 Albstadt earthquake, notwithstanding the differences in the scenario. Through this real-world application we demonstrate the potential of VGI and open data to be used for exposure modelling for natural risk assessment, when combined with suitable knowledge on building fragility and accounting for the inherent uncertainties.

The Lower Rhine Embayment in western Germany is one of the most important areas of earthquake recurrence north of the Alps, facing a moderate level of seismic hazard in the European context but a significant level of risk. This study deals with the impact of a scenario earthquake with a moment magnitude of 6.5 occurring along the Erft fault system south-west to the city of Cologne, the fourth largest German city with more than one million inhabitants and accommodating important industrial facilities. Since the city is located on thick layers of Quarternary sediments, the geological discontinuities at depth will have a significant influence on the duration and the amplification of ground-motion. Based on a new, harmonized and spatially highly resolved, model of the sedimentary cover, the sensitivity of spectral intensity measures to the site response analysis method is assessed employing random vibration theory approaches. Corresponding damage calculations are conducted in terms of European Macroseismic Scale (EMS-98) damage grades. Residential buildings and buildings with mixed residential and commercial occupancy are included in the corresponding calculations only, in line with most seismic risk assessment studies which have traditionally focused on residential typologies. Results from the damage calculations are presented in terms of number of buildings exposed to bands of EMS-98 intensity levels and probabilities of EMS-98 damage grades for residential buildings and buildings with mixed residential and commercial occupancy. Casualties in the city and the neighboring districts are estimated by means of the PAGER empirical method using population counts at the quartier (“Stadtviertel”) level in Cologne, the “Bezirk” level in Bonn and Aachen and at the municipality (“Gemeinde”) level for the surrounding areas, all of these comprised within the district (“Regierungsbezirk”) of Cologne.

Appeared in: Open Search Symposium 2019, 23-24 October 2019, Garching / Munich, Germany. &nbsp

The Global Earthquake Activity Rate (GEAR1) seismicity model uses an optimized combination of geodetic strain rates, hypotheses about converting strain rates to seismicity rates from plate tectonics, and earthquake-catalog data to estimate global mw ≥ 5.767$ shallow (≤ 70 km) seismicity rates. It comprises two parent models: a strain rate-based model, and a smoothed-seismicity based model. The GEAR1 model was retrospectively evaluated and calibrated using earthquake data from 2005-2012, resulting in a preferred log-linear, multiplicative combination of the parent forecasts. Since October 1, 2015, the GEAR1 model has undergone prospective evaluation within the Collaboratory for the Study of Earthquake Predictability (CSEP) testing center. We present initial prospective forecast test results for the GEAR1 model, its tectonic and seismicity components, and for the first iteration of the strain rate-based model, during the period October 1, 2015 to September 7, 2017. During the evaluation period, observed earthquakes are consistent with the GEAR1 forecast and comparative test results likewise support that GEAR1 is more informative than either of its components alone. Based on a combination of retrospective and prospective testing, the tectonic forecasts do not effectively anticipate observed spatial earthquake distribution, largely due to over-localization of the model with respect to observed earthquake distributions.

System Erde; 8, Involving citizens into science has long tradition; examples are bird watching or weather observation. Digital technologies foster citizen science by providing easy to use apps via the Internet serving specific scientific tasks. At GFZ we develop and test approaches to receive data provided by citizens for various scientific applications: Examples are the quantification of light pollution during the night, and the assessment of damages to buildings caused by flood. Data provided by citizens should complement scientific data collected by sensors and thus overcome shortcomings sensors have. In the case of quantifying light pollution an app allows participants to use their visual system as a sensor to measure the brightness of the sky. The app directs participants to find a particular star in the sky, and then report whether it is visible or not. To assess flood damage we develop a system that filters relevant images from the huge amount of information available in social media and to extract suitable information such as inundation depth and area or water pollution. It also supports scientists to judge reliability and usefulness of the information derived from social media. In several studies we already could show the scientific value of data provided by citizens.

Earthquake forecasting models express hypotheses about seismogenesis that underpin global and regional probabilistic seismic hazard assessments (PSHAs). An implicit assumption is that the comparatively higher spatiotemporal resolution datasets from which regional models are generated lead to more informative seismicity forecasts than global models, which are however calibrated on greater datasets of large earthquakes. Here, we prospectively assess the ability of the Global Earthquake Activity Rate (GEAR1) model and 19 time-independent regional models to forecast M 4.95+ seismicity in California, New Zealand, and Italy from 2014 through 2021, using metrics developed by the Collaboratory for the Study of Earthquake Predictability (CSEP). Our results show that regional models that adaptively smooth small earthquake locations perform best in California and Italy during the evaluation period; however, GEAR1, based on global seismicity and geodesy datasets, performs surprisingly well across all testing regions, ranking first in New Zealand, second in California, and third in Italy. Furthermore, the performance of the models is highly sensitive to spatial smoothing, and the optimal smoothing likely depends on the regional tectonic setting. Acknowledging the limited prospective test data, these results provide preliminary support for using GEAR1 as a global reference M 4.95+ seismicity model that could inform eight-year regional and global PSHAs.

Finite-fault earthquake source inversions infer the (time-dependent) displacement on the rupture surface from geophysical data. The resulting earthquake source models document the complexity of the rupture process. However, multiple source models for the same earthquake, obtained by different research teams, often exhibit remarkable dissimilarities. To address the uncertainties in earthquake-source inversion methods and to understand strengths and weaknesses of the various approaches used, the Source Inversion Validation (SIV) project conducts a set of forward-modeling exercises and inversion benchmarks. In this article, we describe the SIV strategy, the initial benchmarks, and current SIV results. Furthermore, we apply statistical tools for quantitative waveform comparison and for investigating source-model (dis)similarities that enable us to rank the solutions, and to identify particularly promising source inversion approaches. All SIV exercises (with related data and descriptions) and statistical comparison tools are available via an online collaboration platform, and we encourage source modelers to use the SIV benchmarks for developing and testing new methods. We envision that the SIV efforts will lead to new developments for tackling the earthquake-source imaging problem.