Seismic hazard and risk assessment based on Unified Scaling Law for Earthquakes: thirteen principal urban agglomerations of India

Seismic hazard and risk assessment based on Unified Scaling Law for Earthquakes: thirteen... The deterministic seismic hazard map of India with spatially distributed peak ground acceleration was used to estimate seismic risk using two data sets of the Indian population—the model population data set and the data set based on India’s Census 2011. Four series of the earthquake risk maps of the region based on these two population density sets were cross-compared. The discrepancy of the population data and seismic risks estimation were illuminated for the thirteen principal urban agglomerations of India. The confirmed fractal nature of earthquakes and their distribution in space implies that traditional probabilistic estimations of seismic hazard and risks of cities and urban agglomerations are usually underestimated. The evident patterns of distributed seismic activity follow the Unified Scaling Law for Earthquakes, USLE, which generalizes Gutenberg–Richter recurrence relation. The results of the systematic global analysis imply that the occurrence of earthquakes in a region is characterized with USLE: log10 N (M, L) = A + B × (5 − M) + C × log10 L, where N(M, L)—expected annual number of earthquakes of magnitude M within an area of liner size L, A determines seismic static rate, B—balance between magnitude ranges, and C—fractal dimension of seismic loci. We apply the seismic hazard and risk assessment methodology developed recently based on USLE, pattern recognition of earthquake-prone geomorphic nodes, and neo-deterministic scenarios of destructive ground shaking. Objects of risk are individuals (1) as reported in the 2011 National Census data and (2) as predicted for 2010 by Gridded Population of the World (model GPWv3); vulnerability depends nonlinearly on population density. The resulting maps of seismic hazard and different risk estimates based on population density are cross-compared. To avoid misleading interpretations, we emphasize that risk estimates presented here for academic purposes only. In the matter of fact, they confirm that estimations addressing more realistic and practical kinds of seismic risks should involve experts in distribution of objects of risk of different vulnerability, i.e., specialists in earthquake engineering, social sciences, and economics. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Natural Hazards Springer Journals

Seismic hazard and risk assessment based on Unified Scaling Law for Earthquakes: thirteen principal urban agglomerations of India

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media B.V., part of Springer Nature
Subject
Earth Sciences; Natural Hazards; Hydrogeology; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Civil Engineering; Environmental Management
ISSN
0921-030X
eISSN
1573-0840
D.O.I.
10.1007/s11069-018-3261-8
Publisher site
See Article on Publisher Site

Abstract

The deterministic seismic hazard map of India with spatially distributed peak ground acceleration was used to estimate seismic risk using two data sets of the Indian population—the model population data set and the data set based on India’s Census 2011. Four series of the earthquake risk maps of the region based on these two population density sets were cross-compared. The discrepancy of the population data and seismic risks estimation were illuminated for the thirteen principal urban agglomerations of India. The confirmed fractal nature of earthquakes and their distribution in space implies that traditional probabilistic estimations of seismic hazard and risks of cities and urban agglomerations are usually underestimated. The evident patterns of distributed seismic activity follow the Unified Scaling Law for Earthquakes, USLE, which generalizes Gutenberg–Richter recurrence relation. The results of the systematic global analysis imply that the occurrence of earthquakes in a region is characterized with USLE: log10 N (M, L) = A + B × (5 − M) + C × log10 L, where N(M, L)—expected annual number of earthquakes of magnitude M within an area of liner size L, A determines seismic static rate, B—balance between magnitude ranges, and C—fractal dimension of seismic loci. We apply the seismic hazard and risk assessment methodology developed recently based on USLE, pattern recognition of earthquake-prone geomorphic nodes, and neo-deterministic scenarios of destructive ground shaking. Objects of risk are individuals (1) as reported in the 2011 National Census data and (2) as predicted for 2010 by Gridded Population of the World (model GPWv3); vulnerability depends nonlinearly on population density. The resulting maps of seismic hazard and different risk estimates based on population density are cross-compared. To avoid misleading interpretations, we emphasize that risk estimates presented here for academic purposes only. In the matter of fact, they confirm that estimations addressing more realistic and practical kinds of seismic risks should involve experts in distribution of objects of risk of different vulnerability, i.e., specialists in earthquake engineering, social sciences, and economics.

Journal

Natural HazardsSpringer Journals

Published: Mar 13, 2018

References

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