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Empirical Models of Shear-Wave Radiation Pattern Derived from Large Datasets of Ground-Shaking Observations
Shear-waves are the most energetic body-waves radiated from an earthquake, and are responsible for the destruction of engineered structures. In both short-term emergency response and long-term risk forecasting of disaster-resilient built environment, it is critical to predict spatially accurate dist...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6353911/ https://www.ncbi.nlm.nih.gov/pubmed/30700780 http://dx.doi.org/10.1038/s41598-018-37524-4 |
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author | Kotha, Sreeram Reddy Cotton, Fabrice Bindi, Dino |
author_facet | Kotha, Sreeram Reddy Cotton, Fabrice Bindi, Dino |
author_sort | Kotha, Sreeram Reddy |
collection | PubMed |
description | Shear-waves are the most energetic body-waves radiated from an earthquake, and are responsible for the destruction of engineered structures. In both short-term emergency response and long-term risk forecasting of disaster-resilient built environment, it is critical to predict spatially accurate distribution of shear-wave amplitudes. Although decades’ old theory proposes a deterministic, highly anisotropic, four-lobed shear-wave radiation pattern, from lack of convincing evidence, most empirical ground-shaking prediction models settled for an oversimplified stochastic radiation pattern that is isotropic on average. Today, using the large datasets of uniformly processed seismograms from several strike, normal, reverse, and oblique-slip earthquakes across the globe, compiled specifically for engineering applications, we could reveal, quantify, and calibrate the frequency-, distance-, and style-of-faulting dependent transition of shear-wave radiation between a stochastic-isotropic and a deterministic-anisotropic phenomenon. Consequent recalibration of empirical ground-shaking models dramatically improved their predictions: with isodistant anisotropic variations of ±40%, and 8% reduction in uncertainty. The outcomes presented here can potentially trigger a reappraisal of several practical issues in engineering seismology, particularly in seismic ground-shaking studies and seismic hazard and risk assessment. |
format | Online Article Text |
id | pubmed-6353911 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-63539112019-01-31 Empirical Models of Shear-Wave Radiation Pattern Derived from Large Datasets of Ground-Shaking Observations Kotha, Sreeram Reddy Cotton, Fabrice Bindi, Dino Sci Rep Article Shear-waves are the most energetic body-waves radiated from an earthquake, and are responsible for the destruction of engineered structures. In both short-term emergency response and long-term risk forecasting of disaster-resilient built environment, it is critical to predict spatially accurate distribution of shear-wave amplitudes. Although decades’ old theory proposes a deterministic, highly anisotropic, four-lobed shear-wave radiation pattern, from lack of convincing evidence, most empirical ground-shaking prediction models settled for an oversimplified stochastic radiation pattern that is isotropic on average. Today, using the large datasets of uniformly processed seismograms from several strike, normal, reverse, and oblique-slip earthquakes across the globe, compiled specifically for engineering applications, we could reveal, quantify, and calibrate the frequency-, distance-, and style-of-faulting dependent transition of shear-wave radiation between a stochastic-isotropic and a deterministic-anisotropic phenomenon. Consequent recalibration of empirical ground-shaking models dramatically improved their predictions: with isodistant anisotropic variations of ±40%, and 8% reduction in uncertainty. The outcomes presented here can potentially trigger a reappraisal of several practical issues in engineering seismology, particularly in seismic ground-shaking studies and seismic hazard and risk assessment. Nature Publishing Group UK 2019-01-30 /pmc/articles/PMC6353911/ /pubmed/30700780 http://dx.doi.org/10.1038/s41598-018-37524-4 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Kotha, Sreeram Reddy Cotton, Fabrice Bindi, Dino Empirical Models of Shear-Wave Radiation Pattern Derived from Large Datasets of Ground-Shaking Observations |
title | Empirical Models of Shear-Wave Radiation Pattern Derived from Large Datasets of Ground-Shaking Observations |
title_full | Empirical Models of Shear-Wave Radiation Pattern Derived from Large Datasets of Ground-Shaking Observations |
title_fullStr | Empirical Models of Shear-Wave Radiation Pattern Derived from Large Datasets of Ground-Shaking Observations |
title_full_unstemmed | Empirical Models of Shear-Wave Radiation Pattern Derived from Large Datasets of Ground-Shaking Observations |
title_short | Empirical Models of Shear-Wave Radiation Pattern Derived from Large Datasets of Ground-Shaking Observations |
title_sort | empirical models of shear-wave radiation pattern derived from large datasets of ground-shaking observations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6353911/ https://www.ncbi.nlm.nih.gov/pubmed/30700780 http://dx.doi.org/10.1038/s41598-018-37524-4 |
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