{"title":"为 CENA 使用集合机器学习方法建立中小规模潜在诱发地震的地动模型","authors":"Najme Alidadi, Shahram Pezeshk","doi":"10.1785/0120230242","DOIUrl":null,"url":null,"abstract":"Ground‐motion models (GMMs) are vital in assessing probabilistic seismic hazards and uncertainties. This study develops new GMMs benefiting from nonparametric machine learning algorithms, including artificial neural networks, kernel ridge, random forest, and gradient boosting regression techniques for small‐to‐moderate potentially induced earthquakes in central and eastern North America (CENA). As part of this study, we evaluate the performance of different machine learning models in estimating peak ground acceleration (PGA) and 17 spectral accelerations based on the moment magnitude (Mw), hypocentral distance (Rhypo), and the timed‐average shear‐wave velocity of the upper 30 m of soil (VS30). To train the algorithms, we have utilized a database of nearly 31,000 ground motions with small and moderate moment magnitudes ranging from 3.0 to 5.8, recorded within a hypocentral distance of less than 200 km in CENA. Typically, for GMM development, analysts employ linear regression‐based models with predefined functional forms. The requirement for predefined functional forms can restrict the use of complicated and nonlinear equations to improve performance. Although the conventional regression model is more interpretable, machine learning can achieve a better result given sufficient training data. The results of error metrics reveal that gradient‐boosting regression provides a better performance. Furthermore, a machine learning ensemble method is used to combine the regression results of four machine learning algorithms. The ensemble method improves the GMM performance and provides smoother results.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"58 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ground‐Motion Model for Small‐to‐Moderate Potentially Induced Earthquakes Using an Ensemble Machine Learning Approach for CENA\",\"authors\":\"Najme Alidadi, Shahram Pezeshk\",\"doi\":\"10.1785/0120230242\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ground‐motion models (GMMs) are vital in assessing probabilistic seismic hazards and uncertainties. This study develops new GMMs benefiting from nonparametric machine learning algorithms, including artificial neural networks, kernel ridge, random forest, and gradient boosting regression techniques for small‐to‐moderate potentially induced earthquakes in central and eastern North America (CENA). As part of this study, we evaluate the performance of different machine learning models in estimating peak ground acceleration (PGA) and 17 spectral accelerations based on the moment magnitude (Mw), hypocentral distance (Rhypo), and the timed‐average shear‐wave velocity of the upper 30 m of soil (VS30). To train the algorithms, we have utilized a database of nearly 31,000 ground motions with small and moderate moment magnitudes ranging from 3.0 to 5.8, recorded within a hypocentral distance of less than 200 km in CENA. Typically, for GMM development, analysts employ linear regression‐based models with predefined functional forms. The requirement for predefined functional forms can restrict the use of complicated and nonlinear equations to improve performance. Although the conventional regression model is more interpretable, machine learning can achieve a better result given sufficient training data. The results of error metrics reveal that gradient‐boosting regression provides a better performance. Furthermore, a machine learning ensemble method is used to combine the regression results of four machine learning algorithms. The ensemble method improves the GMM performance and provides smoother results.\",\"PeriodicalId\":9444,\"journal\":{\"name\":\"Bulletin of the Seismological Society of America\",\"volume\":\"58 1\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bulletin of the Seismological Society of America\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1785/0120230242\",\"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":"Bulletin of the Seismological Society of America","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1785/0120230242","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}
Ground‐Motion Model for Small‐to‐Moderate Potentially Induced Earthquakes Using an Ensemble Machine Learning Approach for CENA
Ground‐motion models (GMMs) are vital in assessing probabilistic seismic hazards and uncertainties. This study develops new GMMs benefiting from nonparametric machine learning algorithms, including artificial neural networks, kernel ridge, random forest, and gradient boosting regression techniques for small‐to‐moderate potentially induced earthquakes in central and eastern North America (CENA). As part of this study, we evaluate the performance of different machine learning models in estimating peak ground acceleration (PGA) and 17 spectral accelerations based on the moment magnitude (Mw), hypocentral distance (Rhypo), and the timed‐average shear‐wave velocity of the upper 30 m of soil (VS30). To train the algorithms, we have utilized a database of nearly 31,000 ground motions with small and moderate moment magnitudes ranging from 3.0 to 5.8, recorded within a hypocentral distance of less than 200 km in CENA. Typically, for GMM development, analysts employ linear regression‐based models with predefined functional forms. The requirement for predefined functional forms can restrict the use of complicated and nonlinear equations to improve performance. Although the conventional regression model is more interpretable, machine learning can achieve a better result given sufficient training data. The results of error metrics reveal that gradient‐boosting regression provides a better performance. Furthermore, a machine learning ensemble method is used to combine the regression results of four machine learning algorithms. The ensemble method improves the GMM performance and provides smoother results.
期刊介绍:
The Bulletin of the Seismological Society of America, commonly referred to as BSSA, (ISSN 0037-1106) is the premier journal of advanced research in earthquake seismology and related disciplines. It first appeared in 1911 and became a bimonthly in 1963. Each issue is composed of scientific papers on the various aspects of seismology, including investigation of specific earthquakes, theoretical and observational studies of seismic waves, inverse methods for determining the structure of the Earth or the dynamics of the earthquake source, seismometry, earthquake hazard and risk estimation, seismotectonics, and earthquake engineering. Special issues focus on important earthquakes or rapidly changing topics in seismology. BSSA is published by the Seismological Society of America.