Capturing epistemic uncertainty in the Iranian strong-motion data on the basis of backbone ground motion models
In the current practice of probabilistic seismic hazard analysis (PSHA), the different estimates of ground motions predicted by ground motion models (GMMs) are attributed to epistemic uncertainty. The epistemic uncertainties arise from the lack of knowledge which is reflected in imperfect models and...
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description | In the current practice of probabilistic seismic hazard analysis (PSHA), the different estimates of ground motions predicted by ground motion models (GMMs) are attributed to epistemic uncertainty. The epistemic uncertainties arise from the lack of knowledge which is reflected in imperfect models and can be handled by either logic tree or backbone approaches. The use of backbone approach for PSHA provides a more robust estimation of the GMM contribution to the epistemic uncertainty. In this study, we quantify the epistemic uncertainty in the Iranian strong-motion data by a scale factor that can be calibrated to the recorded strong-motions. The scale factor is then added and subtracted from the backbone GMM to fairly cover the spread in the predictions from other GMMs. For this purpose, we used the Iranian strong-motion database that includes 865 records from 167 events up to 2013, with the moment magnitude range of 5.0 ≤ M ≤ 7.4, and distances up to 120 km including a variety of fault mechanisms. On the other hand, several candidate GMMs were selected from local, regional, and worldwide data. Then, we applied a data-driven method based on the deviance information criterion to rank the candidate GMMs and select the best GMM as the backbone model. The results of this study show that the epistemic uncertainty varies approximately from 0.1 to 0.3 in base-10 logarithmic units. It generally has minima in the magnitude range of prevalent data (M 5.5–6.5) and increases for small (M 4.5–5.5) and large earthquake magnitudes (M 6.5–7.5). The results also show that the scale factors generally grow with distance. Moreover, notable site effects are seen in the Iranian strong-motions. We conclude therefore that the proposed backbone GMMs along with the estimated scales factors of this study are promising for use in future earthquake hazard estimation in Iran, as they capture the recorded data and provide information on the upper and lower bounds of ground motion estimates. |
doi_str_mv | 10.1007/s10950-019-09886-3 |
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The epistemic uncertainties arise from the lack of knowledge which is reflected in imperfect models and can be handled by either logic tree or backbone approaches. The use of backbone approach for PSHA provides a more robust estimation of the GMM contribution to the epistemic uncertainty. In this study, we quantify the epistemic uncertainty in the Iranian strong-motion data by a scale factor that can be calibrated to the recorded strong-motions. The scale factor is then added and subtracted from the backbone GMM to fairly cover the spread in the predictions from other GMMs. For this purpose, we used the Iranian strong-motion database that includes 865 records from 167 events up to 2013, with the moment magnitude range of 5.0 ≤ M ≤ 7.4, and distances up to 120 km including a variety of fault mechanisms. On the other hand, several candidate GMMs were selected from local, regional, and worldwide data. Then, we applied a data-driven method based on the deviance information criterion to rank the candidate GMMs and select the best GMM as the backbone model. The results of this study show that the epistemic uncertainty varies approximately from 0.1 to 0.3 in base-10 logarithmic units. It generally has minima in the magnitude range of prevalent data (M 5.5–6.5) and increases for small (M 4.5–5.5) and large earthquake magnitudes (M 6.5–7.5). The results also show that the scale factors generally grow with distance. Moreover, notable site effects are seen in the Iranian strong-motions. We conclude therefore that the proposed backbone GMMs along with the estimated scales factors of this study are promising for use in future earthquake hazard estimation in Iran, as they capture the recorded data and provide information on the upper and lower bounds of ground motion estimates.</description><identifier>ISSN: 1383-4649</identifier><identifier>EISSN: 1573-157X</identifier><identifier>DOI: 10.1007/s10950-019-09886-3</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Backbone ; Data ; Earth and Environmental Science ; Earth Sciences ; Earthquakes ; Epistemology ; Geological hazards ; Geophysics/Geodesy ; Geotechnical Engineering & Applied Earth Sciences ; Ground motion ; Hazard assessment ; Hydrogeology ; Lower bounds ; Original Article ; Predictions ; Seismic activity ; Seismic analysis ; Seismic hazard ; Seismology ; Structural Geology ; Uncertainty</subject><ispartof>Journal of seismology, 2020-02, Vol.24 (1), p.75-87</ispartof><rights>Springer Nature B.V. 2019</rights><rights>Journal of Seismology is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a342t-bcdfaff44053227274b0f84982a34b392a5cb90613dd07e1627de8a8700a78593</citedby><cites>FETCH-LOGICAL-a342t-bcdfaff44053227274b0f84982a34b392a5cb90613dd07e1627de8a8700a78593</cites><orcidid>0000-0002-4331-4021</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10950-019-09886-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10950-019-09886-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Kowsari, Milad</creatorcontrib><creatorcontrib>Ghasemi, Saeid</creatorcontrib><creatorcontrib>Farajpour, Zoya</creatorcontrib><creatorcontrib>Zare, Mehdi</creatorcontrib><title>Capturing epistemic uncertainty in the Iranian strong-motion data on the basis of backbone ground motion models</title><title>Journal of seismology</title><addtitle>J Seismol</addtitle><description>In the current practice of probabilistic seismic hazard analysis (PSHA), the different estimates of ground motions predicted by ground motion models (GMMs) are attributed to epistemic uncertainty. The epistemic uncertainties arise from the lack of knowledge which is reflected in imperfect models and can be handled by either logic tree or backbone approaches. The use of backbone approach for PSHA provides a more robust estimation of the GMM contribution to the epistemic uncertainty. In this study, we quantify the epistemic uncertainty in the Iranian strong-motion data by a scale factor that can be calibrated to the recorded strong-motions. The scale factor is then added and subtracted from the backbone GMM to fairly cover the spread in the predictions from other GMMs. For this purpose, we used the Iranian strong-motion database that includes 865 records from 167 events up to 2013, with the moment magnitude range of 5.0 ≤ M ≤ 7.4, and distances up to 120 km including a variety of fault mechanisms. On the other hand, several candidate GMMs were selected from local, regional, and worldwide data. Then, we applied a data-driven method based on the deviance information criterion to rank the candidate GMMs and select the best GMM as the backbone model. The results of this study show that the epistemic uncertainty varies approximately from 0.1 to 0.3 in base-10 logarithmic units. It generally has minima in the magnitude range of prevalent data (M 5.5–6.5) and increases for small (M 4.5–5.5) and large earthquake magnitudes (M 6.5–7.5). The results also show that the scale factors generally grow with distance. Moreover, notable site effects are seen in the Iranian strong-motions. We conclude therefore that the proposed backbone GMMs along with the estimated scales factors of this study are promising for use in future earthquake hazard estimation in Iran, as they capture the recorded data and provide information on the upper and lower bounds of ground motion estimates.</description><subject>Backbone</subject><subject>Data</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Earthquakes</subject><subject>Epistemology</subject><subject>Geological hazards</subject><subject>Geophysics/Geodesy</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Ground motion</subject><subject>Hazard assessment</subject><subject>Hydrogeology</subject><subject>Lower bounds</subject><subject>Original Article</subject><subject>Predictions</subject><subject>Seismic activity</subject><subject>Seismic analysis</subject><subject>Seismic hazard</subject><subject>Seismology</subject><subject>Structural Geology</subject><subject>Uncertainty</subject><issn>1383-4649</issn><issn>1573-157X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kE1LxDAQhosouK7-AU8Bz9HJR5vmKIsfCwteFLyFtE3XrNukJulh_73RLnjzMjMwzzsDT1FcE7glAOIuEpAlYCASg6zrCrOTYkFKwXAu76d5ZjXDvOLyvLiIcQeQMckWhV_pMU3Bui0yo43JDLZFk2tNSNq6dEDWofRh0DpoZ7VDMQXvtnjwyXqHOp008jPR6Ggj8n0e2s_GO4O2wU-uQ0d28J3Zx8virNf7aK6OfVm8PT68rp7x5uVpvbrfYM04Tbhpu173PedQMkoFFbyBvuaypnnfMEl12TYSKsK6DoQhFRWdqXUtALSoS8mWxc18dwz-azIxqZ2fgssvFWW8YoJQ4JmiM9UGH2MwvRqDHXQ4KALqR6yaxaosVv2KVSyH2ByK4483E_5O_5P6BqUCfIo</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Kowsari, Milad</creator><creator>Ghasemi, Saeid</creator><creator>Farajpour, Zoya</creator><creator>Zare, Mehdi</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7TG</scope><scope>7TN</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-4331-4021</orcidid></search><sort><creationdate>20200201</creationdate><title>Capturing epistemic uncertainty in the Iranian strong-motion data on the basis of backbone ground motion models</title><author>Kowsari, Milad ; Ghasemi, Saeid ; Farajpour, Zoya ; Zare, Mehdi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a342t-bcdfaff44053227274b0f84982a34b392a5cb90613dd07e1627de8a8700a78593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Backbone</topic><topic>Data</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Earthquakes</topic><topic>Epistemology</topic><topic>Geological hazards</topic><topic>Geophysics/Geodesy</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>Ground motion</topic><topic>Hazard assessment</topic><topic>Hydrogeology</topic><topic>Lower bounds</topic><topic>Original Article</topic><topic>Predictions</topic><topic>Seismic activity</topic><topic>Seismic analysis</topic><topic>Seismic hazard</topic><topic>Seismology</topic><topic>Structural Geology</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kowsari, Milad</creatorcontrib><creatorcontrib>Ghasemi, Saeid</creatorcontrib><creatorcontrib>Farajpour, Zoya</creatorcontrib><creatorcontrib>Zare, Mehdi</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database (ProQuest)</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>Journal of seismology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kowsari, Milad</au><au>Ghasemi, Saeid</au><au>Farajpour, Zoya</au><au>Zare, Mehdi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Capturing epistemic uncertainty in the Iranian strong-motion data on the basis of backbone ground motion models</atitle><jtitle>Journal of seismology</jtitle><stitle>J Seismol</stitle><date>2020-02-01</date><risdate>2020</risdate><volume>24</volume><issue>1</issue><spage>75</spage><epage>87</epage><pages>75-87</pages><issn>1383-4649</issn><eissn>1573-157X</eissn><abstract>In the current practice of probabilistic seismic hazard analysis (PSHA), the different estimates of ground motions predicted by ground motion models (GMMs) are attributed to epistemic uncertainty. The epistemic uncertainties arise from the lack of knowledge which is reflected in imperfect models and can be handled by either logic tree or backbone approaches. The use of backbone approach for PSHA provides a more robust estimation of the GMM contribution to the epistemic uncertainty. In this study, we quantify the epistemic uncertainty in the Iranian strong-motion data by a scale factor that can be calibrated to the recorded strong-motions. The scale factor is then added and subtracted from the backbone GMM to fairly cover the spread in the predictions from other GMMs. For this purpose, we used the Iranian strong-motion database that includes 865 records from 167 events up to 2013, with the moment magnitude range of 5.0 ≤ M ≤ 7.4, and distances up to 120 km including a variety of fault mechanisms. On the other hand, several candidate GMMs were selected from local, regional, and worldwide data. Then, we applied a data-driven method based on the deviance information criterion to rank the candidate GMMs and select the best GMM as the backbone model. The results of this study show that the epistemic uncertainty varies approximately from 0.1 to 0.3 in base-10 logarithmic units. It generally has minima in the magnitude range of prevalent data (M 5.5–6.5) and increases for small (M 4.5–5.5) and large earthquake magnitudes (M 6.5–7.5). The results also show that the scale factors generally grow with distance. Moreover, notable site effects are seen in the Iranian strong-motions. We conclude therefore that the proposed backbone GMMs along with the estimated scales factors of this study are promising for use in future earthquake hazard estimation in Iran, as they capture the recorded data and provide information on the upper and lower bounds of ground motion estimates.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10950-019-09886-3</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-4331-4021</orcidid></addata></record> |
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subjects | Backbone Data Earth and Environmental Science Earth Sciences Earthquakes Epistemology Geological hazards Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Ground motion Hazard assessment Hydrogeology Lower bounds Original Article Predictions Seismic activity Seismic analysis Seismic hazard Seismology Structural Geology Uncertainty |
title | Capturing epistemic uncertainty in the Iranian strong-motion data on the basis of backbone ground motion models |
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