C. Ren, Claudia Hulbert, P. Johnson, B. Rouet-Leduc
{"title":"机器学习与故障破裂:综述","authors":"C. Ren, Claudia Hulbert, P. Johnson, B. Rouet-Leduc","doi":"10.31223/osf.io/g2dt8","DOIUrl":null,"url":null,"abstract":"Geophysics has historically been a data-driven field, however in recent years the exponential increase of available data has lead to increased adoption of machine learning techniques and algorithm for analysis, detection and forecasting applications to faulting. This work reviews recent advances in the application of machine learning in the study of fault rupture ranging from the laboratory to Solid Earth.","PeriodicalId":251648,"journal":{"name":"Machine Learning in Geosciences","volume":"86 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Machine learning and fault rupture: a review\",\"authors\":\"C. Ren, Claudia Hulbert, P. Johnson, B. Rouet-Leduc\",\"doi\":\"10.31223/osf.io/g2dt8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Geophysics has historically been a data-driven field, however in recent years the exponential increase of available data has lead to increased adoption of machine learning techniques and algorithm for analysis, detection and forecasting applications to faulting. This work reviews recent advances in the application of machine learning in the study of fault rupture ranging from the laboratory to Solid Earth.\",\"PeriodicalId\":251648,\"journal\":{\"name\":\"Machine Learning in Geosciences\",\"volume\":\"86 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Machine Learning in Geosciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31223/osf.io/g2dt8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machine Learning in Geosciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31223/osf.io/g2dt8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Geophysics has historically been a data-driven field, however in recent years the exponential increase of available data has lead to increased adoption of machine learning techniques and algorithm for analysis, detection and forecasting applications to faulting. This work reviews recent advances in the application of machine learning in the study of fault rupture ranging from the laboratory to Solid Earth.
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