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DC Field | Value | Language |
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dc.citation.endPage | 1564 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 1549 | - |
dc.citation.title | BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA | - |
dc.citation.volume | 112 | - |
dc.contributor.author | Seo, Hwanwoo | - |
dc.contributor.author | Kim, Jisong | - |
dc.contributor.author | Kim, Byungmin | - |
dc.date.accessioned | 2023-12-21T14:08:30Z | - |
dc.date.available | 2023-12-21T14:08:30Z | - |
dc.date.created | 2022-06-24 | - |
dc.date.issued | 2022-06 | - |
dc.description.abstract | Ground-motion prediction models (GMPMs) have been developed to estimate seismic intensity considering earthquake magnitude, source-to-site distance, site condition, and so on. This study proposes GMPMs to predict 5% damped pseudospectral acceleration (PSA) for 27 periods ranging from 0.01 to 10 s in Korea, based on three machine-learning techniques (i.e., artificial neural network [ANN], random forest [RF], and gradient boosting [GB]). We use 1189 ground motions recorded at 50 surface stations during the 77 earthquakes with a local magnitude (M-L) greater than 3.0, including the Gyeongju and Pohang earthquakes with M-L of 5.8 and 5.4, respectively. We compare the performances of the three machine-learning-based models and the classical regression-based model in terms of the coefficient of determination (R-2), root mean square error (RMSE), mean absolute error (MAE), standard deviation of residuals, and between-event and within-event residuals. The GB-based model shows the best performance. In addition, we analyze the working process of the GB-based model using variable importance and partial dependence (PD) plots. Among the five independent variables (M-L, epicentral distance [R-epi], average shear-wave velocity of the upper 30 m [V-s30], focal depth, and slope angle) used in this study, M-L and R-epi are the most influential variables and show strong correlations with PSAs. We apply the GB-based model to three recent earthquakes larger than M-L 3.0, and the model accurately predicts the PSAs at various stations. We also generate maps of estimated PSA (PSA(eSt)) ) values for the four periods (T = 0.01, 0.1, 1, and 3 s) for the scenario earthquake with an M-L of 5.0. We provide a method for training the GB-based model using the Python library, which can enhance the ground-motion prediction not only in Korea but also worldwide, and an executable version of the validated GB-based model. | - |
dc.identifier.bibliographicCitation | BULLETIN OF THE SEISMOLOGICAL SOCIETY OF AMERICA, v.112, no.3, pp.1549 - 1564 | - |
dc.identifier.doi | 10.1785/0120210244 | - |
dc.identifier.issn | 0037-1106 | - |
dc.identifier.scopusid | 2-s2.0-85131222222 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/59003 | - |
dc.identifier.wosid | 000806339300004 | - |
dc.language | 영어 | - |
dc.publisher | SEISMOLOGICAL SOC AMER | - |
dc.title | Machine-Learning-Based Surface Ground-Motion Prediction Models for South Korea with Low-to-Moderate Seismicity | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Geochemistry & Geophysics | - |
dc.relation.journalResearchArea | Geochemistry & Geophysics | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | AVERAGE HORIZONTAL COMPONENT | - |
dc.subject.keywordPlus | RANDOM FOREST | - |
dc.subject.keywordPlus | PARAMETERS | - |
dc.subject.keywordPlus | EQUATIONS | - |
dc.subject.keywordPlus | PGA | - |
dc.subject.keywordPlus | PERIODS | - |
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