Kate Thomas

This paper provides an overview of the Fault Displacement Hazard Initiative (FDHI). The FDHI is a community-based and multi-year research initiative with the goals of developing: (a) a comprehensive empirical database of surface fault displacement measurements and mapped ruptures, and (b) a suite of next-generation fault displacement predictive models. The database was developed by systematically collecting worldwide data from professional literature, followed by an extensive review and quality assurance process. The FDHI database contains 75 surface-rupturing earthquakes ranging from moment magnitude M 4.9 to 8.0 including data from strike-slip, reverse/reverse-oblique, and normal/normal-oblique faulting events. As part of the FDHI Program, employing the database, four new fault displacement models (FDMs) were developed. The FDMs predict principal or aggregate surface displacement, where aggregate is the combined displacement across principal and distributed ruptures. Two models were developed for all styles of faulting, while the other two are for reverse or strike-slip faulting. The new models include improved magnitude scaling and aleatory variability modeling. Compared with pre-existing models, average displacements in the new models are about 40% larger for M ~7, but smaller for lower and higher magnitudes. In addition, the new models predict significantly smaller displacement at low probability of exceedance. Besides the core FDHI tasks, in this article we also provide an overview of other parallel projects completed in coordination with the FDHI Program.

The probability distribution model for principal displacement accommodated on the surface main trace is a critical input to the fault displacement hazard analysis. This article presents a new model for strike-slip ruptures in the moment magnitude ( M ) range of 6 to 8.3. The new model is the outcome of a multi-year research effort to update the widely used model developed by Petersen and others in 2011. Updates include the adoption of the Fault Displacement Hazard Initiative database and enhancements to rupture and displacement data preparation. Statistical formulation and estimation have also been updated substantially. A three-parameter modified normal distribution that we refer to as the negative Exponentially Modified Gaussian distribution is adopted to model the probability distribution of the natural logarithm of principal displacement. Formulation for the mean parameter of the modified normal includes a random earthquake term, a nonlinear scaling relation with M , and an ellipse function for along-main-trace variation. The aleatory variability of the updated model now depends on M as well as site’s along-main-trace position. These updates not only significantly improve the fit to the distribution of the observed displacements but also yield reasonable 95th percentile predictions for M > 7.5 events. Alternative models representing the estimation uncertainty of the M -scaling relation are also developed. These new models are compared to the previous model in terms of percentile predictions and the calculated hazard curves. The steeper hazard curves from the new models yield a lower exceedance rate than the normal-distribution based model developed previously by Petersen and others.

A main goal of probabilistic fault displacement hazard analysis (PFDHA) is to quantify displacement along and across an identified active fault that poses a hazard to nearby infrastructure such as roads, bridges, pipelines, and telecommunications. PFDHA relies on empirical models developed using data sets of displacement measurements and mapped surface rupture traces compiled from past global surface rupturing earthquakes by field surveys or remote sensing. However, current approaches to determine the location of the main rupture trace are subjective and lack repeatability due to different geological interpretations of the often complex network of mapped rupture traces. This subjectivity makes it difficult to compile and analyze displacement measurements and ruptures from multiple events in a consistent manner. This study provides an objective and repeatable approach to define a main rupture trace that can be applied to either field or remote sensing data. The new approach defined here can be used in developing rupture trace connectivity and geometry for use in displacement model developments and for use in objectively defining the input fault trace for assessing fault displacement hazard.

Most human infectious diseases, especially recently emerging pathogens, originate from animals and ongoing disease transmission from animals to people presents a significant global health burden. Recognition of the epidemiologic circumstances involved in zoonotic spillover, amplification and spread of diseases is essential for prioritizing surveillance and predicting future disease emergence risk. We examine the animal hosts and transmission mechanisms involved in spillover of zoonotic viruses to date and discover that viruses with high host plasticity (i.e. taxonomically and ecologically diverse host range) were more likely to amplify viral spillover by secondary human-to-human transmission and have broader geographic spread. Viruses transmitted to humans during practices that facilitate mixing of diverse animal species had significantly higher host plasticity. Our findings suggest that animal-to-human spillover of new viruses that are capable of infecting diverse host species signal emerging disease events with higher pandemic potential in that these viruses are more likely to amplify by human-to-human transmission with spread on a global scale.

The capacity to conduct zoonotic pathogen surveillance in wildlife is critical for the recognition and identification of emerging health threats. The PREDICT project, a component of United States Agency for International Development’s Emerging Pandemic Threats program, has introduced capacity building efforts to increase zoonotic pathogen surveillance in wildlife in global ‘hot spot’ regions where zoonotic disease emergence is likely to occur. Understanding priorities, challenges, and opportunities from the perspectives of the stakeholders is a key component of any successful capacity building program.