{"title":"针对俯冲地震的特定区域 GMM 集合","authors":"Farhad Sedaghati, Shahram Pezeshk","doi":"10.1785/0220230070","DOIUrl":null,"url":null,"abstract":"This study develops data‐driven global and region‐specific ground‐motion models (GMMs) for subduction earthquakes using a weighted average ensemble model to combine four different nonparametric supervised machine‐learning (ML) algorithms, including an artificial neural network, a kernel ridge regressor, a random forest regressor, and a support vector regressor. To achieve this goal, we train individual models using a subset of the Next Generation Attenuation‐Subduction (NGA‐Sub) data set, including 9559 recordings out of 153 interface and intraslab earthquakes recorded at 3202 different stations. A grid search is used to find each model’s best hyperparameters. Then, we use an equally weighted average ensemble approach to combine these four models. Ensemble modeling is a technique that combines the strengths of multiple ML algorithms to mitigate their weaknesses. The ensemble model considers moment magnitude (M), rupture distance (Rrup), time‐averaged shear‐wave velocity in the upper 30 m (VS30), and depth to the top of the rupture plane (Ztor), as well as tectonic and region as input parameters, and predicts various median orientation‐independent horizontal component ground‐motion intensity measures such as peak ground displacement, peak ground velocity, peak ground acceleration, and 5%‐damped pseudospectral acceleration values at spectral periods of 0.01–10 s in log scale. Although no functional form is defined, the response spectra and the distance and magnitude scaling trends of the weighted average ensemble model are consistent and comparable with the NGA‐Sub GMMs, with slightly lower standard deviations. A mixed effects regression analysis is used to partition the total aleatory variability into between‐event, between‐station, and event‐site‐corrected components. The derived global GMMs are applicable to interface earthquakes with M 4.9–9.12, 14≤Rrup≤1000 km, and Ztor≤47 km for sites having VS30values between 95 and 2230 m/s. For intraslab events, the derived global GMMs are applicable to M 4.0–8.0, 28≤Rrup≤1000 km, and 30≤Ztor≤200 km for sites having VS30 values between 95 and 2100 m/s.","PeriodicalId":21687,"journal":{"name":"Seismological Research Letters","volume":"123 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ensemble Region‐Specific GMMs for Subduction Earthquakes\",\"authors\":\"Farhad Sedaghati, Shahram Pezeshk\",\"doi\":\"10.1785/0220230070\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study develops data‐driven global and region‐specific ground‐motion models (GMMs) for subduction earthquakes using a weighted average ensemble model to combine four different nonparametric supervised machine‐learning (ML) algorithms, including an artificial neural network, a kernel ridge regressor, a random forest regressor, and a support vector regressor. To achieve this goal, we train individual models using a subset of the Next Generation Attenuation‐Subduction (NGA‐Sub) data set, including 9559 recordings out of 153 interface and intraslab earthquakes recorded at 3202 different stations. A grid search is used to find each model’s best hyperparameters. Then, we use an equally weighted average ensemble approach to combine these four models. Ensemble modeling is a technique that combines the strengths of multiple ML algorithms to mitigate their weaknesses. The ensemble model considers moment magnitude (M), rupture distance (Rrup), time‐averaged shear‐wave velocity in the upper 30 m (VS30), and depth to the top of the rupture plane (Ztor), as well as tectonic and region as input parameters, and predicts various median orientation‐independent horizontal component ground‐motion intensity measures such as peak ground displacement, peak ground velocity, peak ground acceleration, and 5%‐damped pseudospectral acceleration values at spectral periods of 0.01–10 s in log scale. Although no functional form is defined, the response spectra and the distance and magnitude scaling trends of the weighted average ensemble model are consistent and comparable with the NGA‐Sub GMMs, with slightly lower standard deviations. A mixed effects regression analysis is used to partition the total aleatory variability into between‐event, between‐station, and event‐site‐corrected components. The derived global GMMs are applicable to interface earthquakes with M 4.9–9.12, 14≤Rrup≤1000 km, and Ztor≤47 km for sites having VS30values between 95 and 2230 m/s. For intraslab events, the derived global GMMs are applicable to M 4.0–8.0, 28≤Rrup≤1000 km, and 30≤Ztor≤200 km for sites having VS30 values between 95 and 2100 m/s.\",\"PeriodicalId\":21687,\"journal\":{\"name\":\"Seismological Research Letters\",\"volume\":\"123 1\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Seismological Research Letters\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1785/0220230070\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seismological Research Letters","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1785/0220230070","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0
摘要
本研究使用加权平均集合模型,结合四种不同的非参数监督机器学习(ML)算法,包括人工神经网络、核脊回归器、随机森林回归器和支持向量回归器,为俯冲地震开发数据驱动的全球和特定区域地动模型(GMM)。为了实现这一目标,我们使用下一代衰减-减弱(NGA-Sub)数据集的子集来训练各个模型,其中包括在 3202 个不同站点记录的 153 次界面和实验室内地震中的 9559 次记录。我们使用网格搜索来找到每个模型的最佳超参数。然后,我们使用加权平均集合方法来组合这四个模型。集合建模是一种结合多种 ML 算法的优点以减轻其缺点的技术。集合模型将力矩大小(M)、断裂距离(Rrup)、上部 30 米的时间平均剪切波速度(VS30)、到断裂面顶部的深度(Ztor)以及构造和区域作为输入参数,并预测各种与方位无关的水平分量地动强度中值,如地表位移峰值、地表速度峰值、地表加速度峰值以及频谱周期为 0.01-10 秒的对数标度。虽然没有定义函数形式,但加权平均集合模型的响应谱以及距离和幅度缩放趋势与 NGA-Sub GMMs 一致并具有可比性,标准偏差略低。通过混合效应回归分析,将总的人工变异性划分为事件间、站点间和事件-站点校正部分。推导出的全球 GMM 适用于 VS30 值在 95 至 2230 m/s 之间的站点,M 值为 4.9-9.12、14≤Rrup≤1000 km 和 Ztor≤47 km 的界面地震。对于台内事件,得出的全球 GMM 适用于 M 4.0-8.0、28≤Rrup≤1000 km 和 30≤Ztor≤200 km(VS30 值在 95 至 2100 m/s 之间)的站点。
Ensemble Region‐Specific GMMs for Subduction Earthquakes
This study develops data‐driven global and region‐specific ground‐motion models (GMMs) for subduction earthquakes using a weighted average ensemble model to combine four different nonparametric supervised machine‐learning (ML) algorithms, including an artificial neural network, a kernel ridge regressor, a random forest regressor, and a support vector regressor. To achieve this goal, we train individual models using a subset of the Next Generation Attenuation‐Subduction (NGA‐Sub) data set, including 9559 recordings out of 153 interface and intraslab earthquakes recorded at 3202 different stations. A grid search is used to find each model’s best hyperparameters. Then, we use an equally weighted average ensemble approach to combine these four models. Ensemble modeling is a technique that combines the strengths of multiple ML algorithms to mitigate their weaknesses. The ensemble model considers moment magnitude (M), rupture distance (Rrup), time‐averaged shear‐wave velocity in the upper 30 m (VS30), and depth to the top of the rupture plane (Ztor), as well as tectonic and region as input parameters, and predicts various median orientation‐independent horizontal component ground‐motion intensity measures such as peak ground displacement, peak ground velocity, peak ground acceleration, and 5%‐damped pseudospectral acceleration values at spectral periods of 0.01–10 s in log scale. Although no functional form is defined, the response spectra and the distance and magnitude scaling trends of the weighted average ensemble model are consistent and comparable with the NGA‐Sub GMMs, with slightly lower standard deviations. A mixed effects regression analysis is used to partition the total aleatory variability into between‐event, between‐station, and event‐site‐corrected components. The derived global GMMs are applicable to interface earthquakes with M 4.9–9.12, 14≤Rrup≤1000 km, and Ztor≤47 km for sites having VS30values between 95 and 2230 m/s. For intraslab events, the derived global GMMs are applicable to M 4.0–8.0, 28≤Rrup≤1000 km, and 30≤Ztor≤200 km for sites having VS30 values between 95 and 2100 m/s.