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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 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.