Considering large earthquake clustering in seismic risk analysis

Earthquakes cluster in space and time resulting in nonlinear damage effects. We compute earthquake interactions using the Coulomb stress transfer theory and dynamic vulnerability from the concept of ductility capacity reduction. We combine both processes in the generic multi-risk framework where risk scenarios are simulated using a variant of the Markov chain Monte Carlo method. We apply the proposed approach to the thrust fault system of northern Italy, considering earthquakes with characteristic magnitudes in the range ~[6, 6.5], different levels of tectonic loading \(\dot{\tau }\) = {10⁻⁴, 10⁻³, 10⁻²} bar/year and a generic stock of fictitious low-rise buildings with different ductility capacities μ _Δ = {2, 4, 6}. We describe the process’ stochasticity by non-stationary Poisson earthquake probabilities and by binomial damage state probabilities. We find that earthquake clustering yields a tail fattening of the seismic risk curve, the effect of which is amplified by damage-dependent fragility due to clustering. The impact of clustering alone is in average more important than dynamic vulnerability, the spatial extent of the former phenomenon being greater than of the latter one.

     

A three-level framework for multi-risk assessment

The effective management of the risks posed by natural and man-made hazards requires all relevant threats and their interactions to be considered. This paper proposes a three-level framework for multi-risk assessment that accounts for possible hazard and risk interactions. The first level is a flow chart that guides the user in deciding whether a multi-hazard and risk approach is required. The second level is a semi-quantitative approach to explore if a more detailed, quantitative assessment is needed. The third level is a detailed quantitative multi-risk analysis based on Bayesian networks. Examples that demonstrate the application of the method are presented.     

Spatiotemporal evolution of the completeness magnitude of the Icelandic earthquake catalogue from 1991 to 2013

In 1991, a digital seismic monitoring network was installed in Iceland with a digital seismic system and automatic operation. After 20 years of operation, we explore for the first time its nationwide performance by analysing the spatiotemporal variations of the completeness magnitude. We use the Bayesian magnitude of completeness (BMC) method that combines local completeness magnitude observations with prior information based on the density of seismic stations. Additionally, we test the impact of earthquake location uncertainties on the BMC results, by filtering the catalogue using a multivariate analysis that identifies outliers in the hypocentre error distribution. We find that the entire North-to-South active rift zone shows a relatively low magnitude of completeness M_c in the range 0.5–1.0, highlighting the ability of the Icelandic network to detect small earthquakes. This work also demonstrates the influence of earthquake location uncertainties on the spatiotemporal magnitude of completeness analysis.     

Ergodicity and Earthquake Catalogs: Forecast Testing and Resulting Implications

Recently the equilibrium property of ergodicity was identified in an earthquake fault system (Tiampo et al., Phys. Rev. Lett. 91, 238501, 2003; Phys. Rev. E 75, 066107, 2007). Ergodicity in this context not only requires that the system is stationary for these networks at the applicable spatial and temporal scales, but also implies that they are in a state of metastable equilibrium, one in which the ensemble averages can be substituted for temporal averages when studying their behavior in space and time. In this work we show that this property can be used to identify those regions of parameter space which are stationary when applied to the seismicity of two naturally-occurring earthquake fault networks. We apply this measure to one particular seismicity-based forecasting tool, the Pattern Informatics index (Tiampo et al., Europhys. Lett. 60, 481–487, 2002; Rundle et al., Proc. National Acad. Sci., U.S.A., Suppl. 1, 99, 2463, 2002), in order to test the hypothesis that the identification of ergodic regions can be used to improve and optimize forecasts that rely on historic seismicity catalogs. We also apply the same measure to synthetic catalogs in order to better understand the physical process that affects this accuracy. We show that, in particular, ergodic regions defined by magnitude and time period provide more reliable forecasts of future events in both natural and synthetic catalogs, and that these improvements can be directly related to specific features or properties of the catalogs that impact the behavior of their spatial and temporal statistics.     

Considering large earthquake clustering in seismic risk analysis

Earthquakes cluster in space and time resulting in nonlinear damage effects. We compute earthquake interactions using the Coulomb stress transfer theory and dynamic vulnerability from the concept of ductility capacity reduction. We combine both processes in the generic multi-risk framework where risk scenarios are simulated using a variant of the Markov chain Monte Carlo method. We apply the proposed approach to the thrust fault system of northern Italy, considering earthquakes with characteristic magnitudes in the range ~[6, 6.5], different levels of tectonic loading \(\dot{\tau }\) = {10^?4, 10^?3, 10^?2} bar/year and a generic stock of fictitious low-rise buildings with different ductility capacities μ _Δ = {2, 4, 6}. We describe the process’ stochasticity by non-stationary Poisson earthquake probabilities and by binomial damage state probabilities. We find that earthquake clustering yields a tail fattening of the seismic risk curve, the effect of which is amplified by damage-dependent fragility due to clustering. The impact of clustering alone is in average more important than dynamic vulnerability, the spatial extent of the former phenomenon being greater than of the latter one.     

Risk assessment of Tunguska-type airbursts

The Tunguska airburst, which devastated a taiga forest over an area greater than 2,000 km² in a remote region of Central Siberia in 1908, is a classic example of extraterrestrial encounter discussed in the asteroid/comet impact hazard and risk assessment literature (e.g. Longo 2007; Carusi et al. 2007). Although it is generally agreed that the cosmic body caused damage by bursting in the air rather than through direct impact on the Earth’s surface, the Tunguska event is often referred to as an impact event. To the best of our knowledge, no detailed studies have been performed to quantify the risk of a similar-sized event over a populated region. We propose here a straightforward probabilistic risk model for Tunguska-type events over the continental United States and use established risk metrics to determine the property (buildings and contents) and human losses. We find an annual average property loss of ~USD 200,000/year, a rate of ~0.3 fatalities/year and ~1.0 injuries/year ranging from a factor 3 below and to a factor 3 above the indicated values when a reasonable rate uncertainty for Tunguska-type events is taken into account. We then illustrate the case of an extreme event over the New York metropolitan area. While we estimate that this “nightmare” scenario would lead to ~USD 1.5 trillion of property loss, ~3.9 millions of fatalities and ~4.7 millions of injuries, such event is almost impossible (occurrence once every ~30 million years) and should only be considered as an illustrative example.     

The quantification of low-probability–high-consequences events: part I. A generic multi-risk approach

Dynamic risk processes, which involve interactions at the hazard and risk levels, have yet to be clearly understood and properly integrated into probabilistic risk assessment. While much attention has been given to this aspect lately, most studies remain limited to a small number of site-specific multi-risk scenarios. We present a generic probabilistic framework based on the sequential Monte Carlo Method to implement coinciding events and triggered chains of events (using a variant of a Markov chain), as well as time-variant vulnerability and exposure. We consider generic perils based on analogies with real ones, natural and man-made. Each simulated time series corresponds to one risk scenario, and the analysis of multiple time series allows for the probabilistic assessment of losses and for the recognition of more or less probable risk paths, including extremes or low-probability–high-consequences chains of events. We find that extreme events can be captured by adding more knowledge on potential interaction processes using in a brick-by-brick approach. We introduce the concept of risk migration matrix to evaluate how multi-risk participates to the emergence of extremes, and we show that risk migration (i.e., clustering of losses) and risk amplification (i.e., loss amplification at higher losses) are the two main causes for their occurrence.     

Non-Critical Precursory Accelerating Seismicity Theory (NC PAST) and limits of the power-law fit methodology

Iron formation rocks of Quadrilátero Ferrífero, Brazil, were deformed at greenschist facies. Quartz grains in bedding parallel veins were sheared and deformed by a combination of mechanisms assisted by aqueous fluids. Veins in the outcrop appear to be stretched parallel to the compositional layering. The overall vein shapes resemble those of boundinage and pinch and swell. In thin sections, veins show microstructures similar to those observed in hand samples, where domains of large quartz crystals are pulled apart for several millimeters. The voids between quartz fragments are filled with domains of polycrystalline quartz. The microstructural and orientation data show that the strain imposed on the vein as a rigid and competent layer was not accommodated in the quartz polycrystals exclusively by crystal plastic deformation or dynamic recrystallization. The new grains are strain-free, with straight boundaries and with weak to random crystallographic fabrics. We interpret these features to have resulted from a combination of processes, which included grain boundary sliding accomplished by solution transfer. We propose that the coeval operation of both mechanisms allows the aggregate to deform at higher strain rates without necking of the vein layer in a type of flow similar to those described in superplastic regimes.