Seismic Hazard Assessment and Prediction Problem in a Big Data World | INSTITUT DE PHYSIQUE DU GLOBE DE PARIS

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  Seismic Hazard Assessment and Prediction Problem in a Big Data World

Friday 15 February 2019
Séminaires Physique des Sites Naturels
Vladimir Kossobokov
(1 Institute of Earthquake Prediction Theory & Mathematical Geophysics, RAS, Moscow)
Extrait: 

The digital revolution started just about 15 years ago has already surpassed the global information storage capacity of more than 5000 Exabytes (5 × 1021 bytes) per year. Open data in a Big Data World provides unprecedented opportunities for enhancing studies of the Earth System. However, it also opens wide avenues for deceptive associations in inter- and transdisciplinary data and, therefore, for inflicted misleading predictions. Seismic hazard assessment is not an easy task that implies a delicate application of Statistics. Regretfully, in many cases of probabilistic PSHA or deterministic DSHA seismic hazard assessment, from term-less (SHA) to time-dependent (t-DASH), and short-term earthquake forecasting (StEF), the claims of a high potential of the method are based on a flawed application of Statistics and, therefore, are hardly suitable for responsible communication to decision makers. In particular, none of the proposed short-term precursory signals showed sufficient evidence to be used as a reliable precursor of catastrophic earthquakes. Evidently, testing must be done (but eventually not performed) in advance claiming prediction of hazardous areas and/or times. Simple tools of Earthquake Prediction Strategies, including Error Diagram and Seismic Roulette null-hypothesis as a metric of the alerted territory, are available to check any SHA method. The resulted set of errors, i.e. the rates of (i) failure-to-predict and (ii) the alerted space-time volume, can be easily compared to random guessing, which comparison permits evaluating the SHA method effectiveness and determining the optimal choice of parameters in regard to a user-defined cost-benefit function. These and other information obtained in such a testing provides realistic estimates of confidence and accuracy of SHA predictions and, if reliable but not necessarily perfect, with related recommendations on the level of risks for decision making in regard to engineering design, insurance, and emergency management.
A few examples of independent expertize of “seismic hazard maps”, “precursors”, and “forecast/prediction methods” are provided.