This paper is concerned with the novel short-term and operational-term seismic hazard assessment approach within the critical service operators and the manufacturing industry. The Cosmetecor earthquake prediction methodology has been tested and validated in the recent two decades. A prototype, Kuznetsov method, for exploring the Earth's interior has been used to create global monitoring network, which automatically detects spatial-temporal clusters and identifies electric potential anomalies. Research team developed the mathematical modelling of proton migration in terms of the fundamental Vlasov-Maxwell equation to convert original time series into visualization of electromagnetic wave. A 2-layer neural network model is used to fine-grained classification. Further, the statistical and scaling laws of seismicity have been exploited to present case of earthquake seasonality, i.e., a dataset of abnormal seismic scenarios for machine learning task. Finally, authors evaluated results in terms of reliability and accuracy of earthquake warnings at M5.2 threshold in Kamchatka: 17% of all warning represent missed alerts, and 83% represent correct alerts where events occurred in a 10-year time horizon. Common outcome in almost every case is mean lead time (time horizon) of 11.62 days. The dispersion is 6.7 days. Further, a non-random sample of the Italian companies assessed new benefits of methodology during survey. The stakeholders confirmed that they will be able to activate business continuity plan to mitigate earthquake consequences in a specific time frame. It is anticipated the emergence of new risk management practices on the Cosmetecor-based high technology of the 21st century, and the replacement of the long-term, one-in-a-hundred-year return period, assessment with a short-term, seasonal, seismic risk assessment.

Towards a new seismic short-term prediction methodology for critical service operators and manufacturing companies against earthquake

V. Bobrovskiy;P. Trucco
2022-01-01

Abstract

This paper is concerned with the novel short-term and operational-term seismic hazard assessment approach within the critical service operators and the manufacturing industry. The Cosmetecor earthquake prediction methodology has been tested and validated in the recent two decades. A prototype, Kuznetsov method, for exploring the Earth's interior has been used to create global monitoring network, which automatically detects spatial-temporal clusters and identifies electric potential anomalies. Research team developed the mathematical modelling of proton migration in terms of the fundamental Vlasov-Maxwell equation to convert original time series into visualization of electromagnetic wave. A 2-layer neural network model is used to fine-grained classification. Further, the statistical and scaling laws of seismicity have been exploited to present case of earthquake seasonality, i.e., a dataset of abnormal seismic scenarios for machine learning task. Finally, authors evaluated results in terms of reliability and accuracy of earthquake warnings at M5.2 threshold in Kamchatka: 17% of all warning represent missed alerts, and 83% represent correct alerts where events occurred in a 10-year time horizon. Common outcome in almost every case is mean lead time (time horizon) of 11.62 days. The dispersion is 6.7 days. Further, a non-random sample of the Italian companies assessed new benefits of methodology during survey. The stakeholders confirmed that they will be able to activate business continuity plan to mitigate earthquake consequences in a specific time frame. It is anticipated the emergence of new risk management practices on the Cosmetecor-based high technology of the 21st century, and the replacement of the long-term, one-in-a-hundred-year return period, assessment with a short-term, seasonal, seismic risk assessment.
2022
Proceedings of the 32nd European Safety and Reliability Conference (ESREL 2022)
978-981-18-5183-4
Seismic Risk, Reinsurance, Seasonality, Risk management
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1222346
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