Errors in expected human losses due to incorrect seismic hazard estimates

Wyss, Max; Nekrasova, Anastasia; Kossobokov, Vladimir

doi:10.1007/s11069-012-0125-5

Cited by 113 publications

(61 citation statements)

References 14 publications

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“…Nekrasova 2011, 2012;Wyss et al 2012). The authors of these studies concluded that the common methods of PSHA are inadequate and need to be revised and probably modified.…”

Section: Introductionmentioning

confidence: 99%

Effect of alternative distributions of ground motion variability on results of probabilistic seismic hazard analysis

Pavlenko

2015

Nat Hazards

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Probabilistic seismic hazard analysis (PSHA) is a regularly applied practice that precedes the construction of important engineering structures. The Cornell-McGuire procedure is the most frequently applied method of PSHA. This paper examines the fundamental assumption of the Cornell-McGuire procedure for PSHA, namely, the log-normal distribution of the residuals of the ground motion parameters. Although the assumption of log-normality is standard, it has not been rigorously tested. Moreover, the application of the unbounded log-normal distribution for the calculation of the hazard curves results in non-zero probabilities of the exceedance of physically unrealistic amplitudes of ground motion parameters. In this study, the distribution of the residuals of the logarithm of peak ground acceleration is investigated, using the database of the Strong-motion Seismograph Networks of Japan and the ground motion prediction equation of Zhao and co-authors. The distribution of residuals is modelled by a number of probability distributions, and the one parametric law that approximates the distribution most precisely is chosen by the statistical criteria. The results of the analysis show that the most accurate approximation is achieved with the generalized extreme value distribution for a central part of a distribution and the generalized Pareto distribution for its upper tail. The effect of replacing a log-normal distribution in the main equation of the Cornell-McGuire method is demonstrated by the calculation of hazard curves for a simple hypothetical example. These hazard curves differ significantly from one another, especially at low annual exceedance probabilities.

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“…Nekrasova 2011, 2012;Wyss et al 2012). The authors of these studies concluded that the common methods of PSHA are inadequate and need to be revised and probably modified.…”

Section: Introductionmentioning

confidence: 99%

Effect of alternative distributions of ground motion variability on results of probabilistic seismic hazard analysis

Pavlenko

2015

Nat Hazards

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“…In the last few years, disastrous earthquakes in Sumatra, Italy, Haiti, Japan, and New Zealand have reinvigorated this debate (34)(35)(36)(37)(38)(39). Many practical deficiencies have been noted, not the least of which is the paucity of data for retrospective calibration and prospective testing of PSHA models, owing to the short span of observations relative to the forecasting time scale (40,41).…”

Section: Probabilistic Seismic Hazard Analysismentioning

confidence: 99%

“…Many practical deficiencies have been noted, not the least of which is the paucity of data for retrospective calibration and prospective testing of PSHA models, owing to the short span of observations relative to the forecasting time scale (40,41). However, some authors have raised the more fundamental question of whether PSHA is misguided because it cannot capture the aleatory variability of large-magnitude earthquakes produced by complex fault systems (35,38,42). Moreover, the pervasive role of subjective probabilities in specifying the epistemic uncertainty in PSHA has made this methodology a target for criticism by scientists who adhere to the frequentist view of probabilities.…”

Section: Probabilistic Seismic Hazard Analysismentioning

confidence: 99%

Testing for ontological errors in probabilistic forecasting models of natural systems

Marzocchi

Jordan

2014

Proc. Natl. Acad. Sci. U.S.A.

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Probabilistic forecasting models describe the aleatory variability of natural systems as well as our epistemic uncertainty about how the systems work. Testing a model against observations exposes ontological errors in the representation of a system and its uncertainties. We clarify several conceptual issues regarding the testing of probabilistic forecasting models for ontological errors: the ambiguity of the aleatory/epistemic dichotomy, the quantification of uncertainties as degrees of belief, the interplay between Bayesian and frequentist methods, and the scientific pathway for capturing predictability. We show that testability of the ontological null hypothesis derives from an experimental concept, external to the model, that identifies collections of data, observed and not yet observed, that are judged to be exchangeable when conditioned on a set of explanatory variables. These conditional exchangeability judgments specify observations with well-defined frequencies. Any model predicting these behaviors can thus be tested for ontological error by frequentist methods; e.g., using P values. In the forecasting problem, prior predictive model checking, rather than posterior predictive checking, is desirable because it provides more severe tests. We illustrate experimental concepts using examples from probabilistic seismic hazard analysis. Severe testing of a model under an appropriate set of experimental concepts is the key to model validation, in which we seek to know whether a model replicates the data-generating process well enough to be sufficiently reliable for some useful purpose, such as long-term seismic forecasting. Pessimistic views of system predictability fail to recognize the power of this methodology in separating predictable behaviors from those that are not.system science | Bayesian statistics | significance testing | subjective probability | expert opinion S cience is rooted in the concept that a model can be tested against observations and rejected when necessary (1). However, the problem of model testing becomes formidable when we consider natural systems. Owing to their scale, complexity, and openness to interactions within a larger environment, most natural systems cannot be replicated in the laboratory, and direct observations of their inner workings are always inadequate. These difficulties raise serious questions about the meaning and feasibility of "model verification" and "model validation" (2), and have led to the pessimistic view that "the outcome of natural processes in general cannot be accurately predicted by mathematical models" (3).Uncertainties in the formal representation of natural systems imply that the forecasting of emergent phenomena such as natural hazards must be based on probabilistic rather than deterministic modeling. The ontological framework for most probabilistic forecasting models comprises two types of uncertainty: an aleatory variability that describes the randomness of the system, and an epistemic uncertainty that characterizes our lack of knowledge about the sy...

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“…Several scientists [e.g., Stein et al, 2012;Wyss et al, 2012] have questioned the reliability of some seismic hazard maps based on the probabilistic seismic hazard analysis (PSHA)-a widely used probabilistic approach that estimates the likelihood of various levels of ground shaking occurring at a given location in a given future time period-raising an intense discussion on this specific point [Hanks et al, 2012;Frankel, 2013;Stein et al, 2013].…”

Section: Seismic Hazard and Public Safety Pages 240-241mentioning

confidence: 99%

“…A profound misconception about PSHA has been made by some scientists [International Seismic Safety Organization, 2012;Wyss et al, 2012;Peresan and Panza, 2012], who have claimed that the probabilistic approach is unacceptable for public safety policy and that citizens have to be protected against the maximum credible earthquake (MCE) no matter how infrequent it may be. Specifically, they propose to replace PSHA with the so-called neo-deterministic seismic hazard assessment (NDSHA), which consists of an MCE-based hazard scenario (hereafter, MCE scenario).…”

Section: Seismic Hazard and Public Safety Pages 240-241mentioning

confidence: 99%

Seismic Hazard and Public Safety

Marzocchi

2013

EoS Transactions

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The recent destructive earthquakes in Wenchuan (China), L'Aquila (Italy), Port‐au‐Prince (Haiti), Christchurch (New Zealand), and Tohoku (Japan) have reignited the discussion over seismic safety. Several scientists [e.g., Stein et al., 2012; Wyss et al., 2012] have questioned the reliability of some seismic hazard maps based on the probabilistic seismic hazard analysis (PSHA)—a widely used probabilistic approach that estimates the likelihood of various levels of ground shaking occurring at a given location in a given future time period—raising an intense discussion on this specific point [Hanks et al., 2012; Frankel, 2013; Stein et al., 2013].

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Errors in expected human losses due to incorrect seismic hazard estimates

Cited by 113 publications

References 14 publications

Effect of alternative distributions of ground motion variability on results of probabilistic seismic hazard analysis

Effect of alternative distributions of ground motion variability on results of probabilistic seismic hazard analysis

Testing for ontological errors in probabilistic forecasting models of natural systems

Seismic Hazard and Public Safety

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