Use of Fourier phase characteristics and effective stress analyses for post-earthquake ground motion estimation: application to ESG6 blind prediction steps 2&3 dataset and JMA accelerometric data
Abstract After the occurrence of a large earthquake, engineering seismologists are often requested to estimate strong ground motions at a site where strong motion data were not obtained. The goal of this study was to test the ability of a class of methods that uses Fourier phase characteristics for...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-06-01
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| Series: | Earth, Planets and Space |
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| Online Access: | https://doi.org/10.1186/s40623-023-01854-z |
| _version_ | 1797795797284159488 |
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| author | Atsushi Nozu |
| author_facet | Atsushi Nozu |
| author_sort | Atsushi Nozu |
| collection | DOAJ |
| description | Abstract After the occurrence of a large earthquake, engineering seismologists are often requested to estimate strong ground motions at a site where strong motion data were not obtained. The goal of this study was to test the ability of a class of methods that uses Fourier phase characteristics for the post-earthquake ground motion estimation, making use of the precious opportunity provide by the ESG6 Blind Prediction Steps 2&3. It was also part of the goal of this study to test the performance of the effective stress analyses to account for soil nonlinearity. In addition to the dataset provided by the organizer of the blind prediction, the author used additional accelerometric data from a nearby JMA site. To simulate ground motions for an M5.9 earthquake at the target site “KUMA”, the Fourier amplitude spectrum was estimated from the spectral ratio between KUMA and the nearby JMA site. The Fourier phase spectrum was approximated by the spectrum of another event at KUMA. Comparison between the estimated and recorded ground motions after the blind prediction revealed that the estimated ground motions were fairly consistent with the observed ground motions, indicating the effectiveness of the method when the rupture process of the target event is simple and the soil nonlinearity at the target site is not significant. To simulate ground motions at KUMA for the M6.5 foreshock and the M7.3 mainshock of the 2016 Kumamoto earthquake sequence, the author conducted effective stress analyses using a program called “FLIP” to account for soil nonlinearity. Comparison between the estimated and recorded ground motions after the blind prediction indicated that the low-frequency components were overestimated and the high-frequency components were underestimated. The strong soil nonlinearity considered in the effective stress analyses was the main cause of the discrepancy. One explanation for this result could be that the nonlinear soil behavior at KUMA during the foreshock and the mainshock was not a strong one. Another explanation could be that the effect of soil nonlinearity was already included in the records at JMA and the effect of soil nonlinearity was double counted in the results submitted by the author. Graphical Abstract |
| first_indexed | 2024-03-13T03:23:27Z |
| format | Article |
| id | doaj.art-ac8284e524724bfca69680b9741f5784 |
| institution | Directory Open Access Journal |
| issn | 1880-5981 |
| language | English |
| last_indexed | 2024-03-13T03:23:27Z |
| publishDate | 2023-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Earth, Planets and Space |
| spelling | doaj.art-ac8284e524724bfca69680b9741f57842023-06-25T11:11:20ZengSpringerOpenEarth, Planets and Space1880-59812023-06-0175112210.1186/s40623-023-01854-zUse of Fourier phase characteristics and effective stress analyses for post-earthquake ground motion estimation: application to ESG6 blind prediction steps 2&3 dataset and JMA accelerometric dataAtsushi Nozu0Director of Earthquake Disaster Prevention Engineering Department, Port and Airport Research InstituteAbstract After the occurrence of a large earthquake, engineering seismologists are often requested to estimate strong ground motions at a site where strong motion data were not obtained. The goal of this study was to test the ability of a class of methods that uses Fourier phase characteristics for the post-earthquake ground motion estimation, making use of the precious opportunity provide by the ESG6 Blind Prediction Steps 2&3. It was also part of the goal of this study to test the performance of the effective stress analyses to account for soil nonlinearity. In addition to the dataset provided by the organizer of the blind prediction, the author used additional accelerometric data from a nearby JMA site. To simulate ground motions for an M5.9 earthquake at the target site “KUMA”, the Fourier amplitude spectrum was estimated from the spectral ratio between KUMA and the nearby JMA site. The Fourier phase spectrum was approximated by the spectrum of another event at KUMA. Comparison between the estimated and recorded ground motions after the blind prediction revealed that the estimated ground motions were fairly consistent with the observed ground motions, indicating the effectiveness of the method when the rupture process of the target event is simple and the soil nonlinearity at the target site is not significant. To simulate ground motions at KUMA for the M6.5 foreshock and the M7.3 mainshock of the 2016 Kumamoto earthquake sequence, the author conducted effective stress analyses using a program called “FLIP” to account for soil nonlinearity. Comparison between the estimated and recorded ground motions after the blind prediction indicated that the low-frequency components were overestimated and the high-frequency components were underestimated. The strong soil nonlinearity considered in the effective stress analyses was the main cause of the discrepancy. One explanation for this result could be that the nonlinear soil behavior at KUMA during the foreshock and the mainshock was not a strong one. Another explanation could be that the effect of soil nonlinearity was already included in the records at JMA and the effect of soil nonlinearity was double counted in the results submitted by the author. Graphical Abstracthttps://doi.org/10.1186/s40623-023-01854-zSite effectFourier phase spectrumSoil nonlinearityEffective stress analysisFLIP |
| spellingShingle | Atsushi Nozu Use of Fourier phase characteristics and effective stress analyses for post-earthquake ground motion estimation: application to ESG6 blind prediction steps 2&3 dataset and JMA accelerometric data Earth, Planets and Space Site effect Fourier phase spectrum Soil nonlinearity Effective stress analysis FLIP |
| title | Use of Fourier phase characteristics and effective stress analyses for post-earthquake ground motion estimation: application to ESG6 blind prediction steps 2&3 dataset and JMA accelerometric data |
| title_full | Use of Fourier phase characteristics and effective stress analyses for post-earthquake ground motion estimation: application to ESG6 blind prediction steps 2&3 dataset and JMA accelerometric data |
| title_fullStr | Use of Fourier phase characteristics and effective stress analyses for post-earthquake ground motion estimation: application to ESG6 blind prediction steps 2&3 dataset and JMA accelerometric data |
| title_full_unstemmed | Use of Fourier phase characteristics and effective stress analyses for post-earthquake ground motion estimation: application to ESG6 blind prediction steps 2&3 dataset and JMA accelerometric data |
| title_short | Use of Fourier phase characteristics and effective stress analyses for post-earthquake ground motion estimation: application to ESG6 blind prediction steps 2&3 dataset and JMA accelerometric data |
| title_sort | use of fourier phase characteristics and effective stress analyses for post earthquake ground motion estimation application to esg6 blind prediction steps 2 3 dataset and jma accelerometric data |
| topic | Site effect Fourier phase spectrum Soil nonlinearity Effective stress analysis FLIP |
| url | https://doi.org/10.1186/s40623-023-01854-z |
| work_keys_str_mv | AT atsushinozu useoffourierphasecharacteristicsandeffectivestressanalysesforpostearthquakegroundmotionestimationapplicationtoesg6blindpredictionsteps23datasetandjmaaccelerometricdata |