The GEM (Global Earthquake Model) Foundation develops hazard and risk models for the calculation of human and economic losses due to earthquakes. These models are important for a wide range of risk management applications, including standards for the design of buildings and infrastructure, insurance/risk transfer, national risk assessments, as well as public risk awareness and education.

The global building exposure model is a mosaic of local and regional models with information regarding the residential, commercial, and industrial building stock at the smallest available administrative division of each country and includes details about the number of buildings, number of occupants, vulnerability characteristics, average built-up area, and average replacement cost. We aimed for a bottom-up approach at the global scale, using national statistics, socio-economic data, and local datasets. This model allows the identification of the most common types of construction worldwide, regions with large fractions of informal construction, and areas prone to earthquakes with a high concentration of population and building stock. The mosaic of exposure models presented herein can be used for the assessment of probabilistic seismic risk and earthquake scenarios. Information at the global, regional, and national levels is available through a public repository (https://github.com/gem/global_exposure_model), which will be used to maintain, update and improve the models.

The Development of a Global Seismic Risk Model was a mammoth undertaking that involved hundreds of people and for the first time presented a detailed view of seismic risk at the global scale. For some developing countries, this was the first time that a seismic risk map was produced, and the associated country profiles are being used by the local authorities.

GEM was founded in 2009 with the purpose of improving the global knowledge of earthquake risk and contributing to the reduction of risk worldwide. In 13 years, GEM has become widely known for its global effort to improve the state of practice of earthquake hazard and risk assessment and for its contribution to improving the state of knowledge of earthquake risk.

The well-established Bath’s law states that the average magnitude difference between a mainshock and its strongest aftershock is roughly 1.2, independently of the size of the mainshock. The main challenge in calculating this value is the bias introduced by missing data points when the strongest aftershock is below the observed cut off magnitude. Ignoring missing values leads to a systematic error, because the data points removed are those with particularly large magnitude differences ∆M. The error is minimized, if we restrict the statistics to mainshocks at least two magnitude units above the cut-off, but then the sample size is strongly reduced. This work provides an innovative approach for modelling ∆M by adapting methods for time-to-event data, which often suffers from incomplete observation (censoring). In doing so, we adequately account for unobserved values and estimate a fully parametric distribution of the magnitude differences ∆M for M ą 6 mainshocks. Results show that magnitude differences are best modeled by the Gompertz distribution, and that larger ∆M are expected at increasing depths and higher heat flows. A simulation experiment suggests that ∆M is mainly driven by the number and the magnitude distribution of aftershocks. Therefore, in a second study, we modelled the variation of aftershock productivity in a stochastically declustered local catalog for New Zealand, using a generalized additive model approach. Results confirm that aftershock counts can be better modelled by a Negative Binomial than a Poisson distribution. Interestingly, there is indication that triggered earthquakes trigger themselves two to three times more aftershocks than comparable

This work investigates a novel approach combining numerical modelling and machine learning, aimed at developing an efficient procedure that can be used for large scale tsunami hazard and risk studies. Probabilistic tsunami hazard and risk assessment are vital tools to understand the risk of tsunami and mitigate its impact, guiding the risk reduction and transfer activities. Such large-scale probabilistic tsunami hazard and risk assessment require many numerically intensive simulations of the possible tsunami events, involving the tsunami phases of generation, wave propagation and inundation on the coast, which are not always feasible without large computational resources like HPCs. In order to undertake such regional PTHA for a larger proportion of the coast, we need to develop concepts and algorithms for reducing the number of events simulated and more rapidly approximate the simulation results needed. This case study for a coastal region of Japan utilizes a limited number of tsunami simulations from submarine earthquakes along the subduction interface to generate a wave propagation database at different depths, and fits these simulation results to a machine learning model to predict the water depth or velocity of the tsunami wave at the coast. Such a hybrid ML-physical model can be further coupled with an inundation scheme to compute the probabilistic tsunami hazard and risk for the onshore region.

We performed benefit-cost analysis to identify optimum retrofitting interventions for the two most vulnerable building typologies in Central America, unreinforced masonry and adobe, considering the direct costs due to building damage and the indirect costs associated with the injured and fatalities. We reviewed worldwide retrofitting techniques, selected those that could be applied in the region for these building types, and derived vulnerability functions considering the impact of each retrofitting intervention in the strength, stiffness, and ductility of the structures. Probabilistic seismic risk analyses were performed considering the original configuration of each building class, as well as the retrofitted version. We calculated average annual losses to estimate the annual savings due to the different structural interventions, and benefit cost ratios were estimated based on the associated cost of each retrofitting technique. Based on the benefit-cost analyses, for a 50-year time horizon and a 4% discount rate, retrofitting these building classes could be economically viable along the western coast of Central America.

Vitor Silva reflects on the current position of seismic risk assessment compared to its hazard counterpart, and posits that this discipline is expected to become common practice in disaster risk management, providing decision makers with valuable information not just about the current threat, but also how the impact of future disasters is expected to evolve. The growth of seismic risk assessment into its adult years will allow a more efficient design and implementation of risk mitigation measures. ultimately contributing to its main and only goal: the reduction of the human and economic losses caused by earthquakes.

This study proposes a framework to forecast the spatial distribution of population and residential buildings for the assessment of future disaster risk. The approach accounts for the number, location, and characteristics of future assets considering sources of aleatory and epistemic uncertainty in several time-dependent variables. The value of the methodology is demonstrated at the urban scale using an earthquake scenario for the Great Metropolitan Area of Costa Rica. Hundreds of trajectories representing future urban growth were generated using geographically weighted regression and multiple-agent systems. These were converted into exposure models featuring the spatial correlation of urban expansion and the densification of the built environment. The forecasted earthquake losses indicate a mean increase in the absolute human and economic losses by 2030. However, the trajectory of relative risk is reducing, suggesting that the long-term enforcement of seismic regulations and urban planning are effectively lowering seismic risk in the case of Costa Rica.

The physical, financial, and social impacts of disasters in Europe are growing and will continue to grow unless urgent actions are taken. In the European Union (EU), during the period from 1980 to 2020, natural disasters affected nearly 50 million people and caused on average an economic loss of roughly €12 billion per year (EEA, 2020). The impacts of flood, wildfire, and extreme heat are increasing rapidly, and climate damages could reach €170 billion per year according to conservative estimates for a 3 scenario unless urgent action is taken now (Szewczyk, et al., 2020). Earthquakes, while rare, have a devastating impact on the ageing buildings and infrastructure of Europe that were constructed prior to modern codes; in Bucharest, for example, nearly 90% of the population lives in multifamily buildings with pre-modern building codes3 (Simpson & Markhvida, 2020). Within the EU, the top-five countries with the highest annual average loss to earthquake are Cyprus, Greece, Romania, Bulgaria, and Croatia, and for floods the top-five countries are Romania, Slovenia, Latvia, Bulgaria, and Austria.4 However, disasters do not affect everyone equally: poor, elderly, very young, and marginalized populations are most affected and least able to recover. In Romania, Greece, Croatia, and Bulgaria, for example, the socio-economic resilience of the poor is on average less than 30% of the national average (World Bank, 2020). Moreover, the local and regional administrations in the poorer and more disadvantaged areas have the least capacity to design and implement resilience investments.