{"title":"JointNet:基于多模态深度学习的瑞利波频散与椭圆度联合反演方法","authors":"Xiang Huang, Ziye Yu, Weitao Wang, Fang Wang","doi":"10.1785/0120230199","DOIUrl":null,"url":null,"abstract":"\n Joint inversion of multitype datasets is an effective approach for high-precision subsurface imaging. We present a new deep learning-based method to jointly invert Rayleigh wave phase velocity and ellipticity into shear-wave velocity of the crust and uppermost mantle. A multimodal deep neural network (termed JointNet) is designed to analyze these two independent physical parameters and generate outputs, including velocity and layer thicknesses. JointNet is trained using random 1D models and corresponding synthetic phase velocity and ellipticity, resulting in a low cost for the training dataset. Evaluation using synthetic and observed data shows that JointNet produces highly comparable results compared to those from a Markov chain Monte Carlo-based method and significantly improves inversion speed. Training using synthetic data ensures its generalized application in various regions with different velocity structures. Moreover, JointNet can be easily extended to include additional datatypes and act as a joint inversion framework to further improve imaging resolution.","PeriodicalId":9444,"journal":{"name":"Bulletin of the Seismological Society of America","volume":"124 6","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"JointNet: A Multimodal Deep Learning-Based Approach for Joint Inversion of Rayleigh Wave Dispersion and Ellipticity\",\"authors\":\"Xiang Huang, Ziye Yu, Weitao Wang, Fang Wang\",\"doi\":\"10.1785/0120230199\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Joint inversion of multitype datasets is an effective approach for high-precision subsurface imaging. We present a new deep learning-based method to jointly invert Rayleigh wave phase velocity and ellipticity into shear-wave velocity of the crust and uppermost mantle. A multimodal deep neural network (termed JointNet) is designed to analyze these two independent physical parameters and generate outputs, including velocity and layer thicknesses. JointNet is trained using random 1D models and corresponding synthetic phase velocity and ellipticity, resulting in a low cost for the training dataset. Evaluation using synthetic and observed data shows that JointNet produces highly comparable results compared to those from a Markov chain Monte Carlo-based method and significantly improves inversion speed. Training using synthetic data ensures its generalized application in various regions with different velocity structures. Moreover, JointNet can be easily extended to include additional datatypes and act as a joint inversion framework to further improve imaging resolution.\",\"PeriodicalId\":9444,\"journal\":{\"name\":\"Bulletin of the Seismological Society of America\",\"volume\":\"124 6\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-12-19\",\"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/10.1785/0120230199\",\"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/10.1785/0120230199","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}
JointNet: A Multimodal Deep Learning-Based Approach for Joint Inversion of Rayleigh Wave Dispersion and Ellipticity
Joint inversion of multitype datasets is an effective approach for high-precision subsurface imaging. We present a new deep learning-based method to jointly invert Rayleigh wave phase velocity and ellipticity into shear-wave velocity of the crust and uppermost mantle. A multimodal deep neural network (termed JointNet) is designed to analyze these two independent physical parameters and generate outputs, including velocity and layer thicknesses. JointNet is trained using random 1D models and corresponding synthetic phase velocity and ellipticity, resulting in a low cost for the training dataset. Evaluation using synthetic and observed data shows that JointNet produces highly comparable results compared to those from a Markov chain Monte Carlo-based method and significantly improves inversion speed. Training using synthetic data ensures its generalized application in various regions with different velocity structures. Moreover, JointNet can be easily extended to include additional datatypes and act as a joint inversion framework to further improve imaging resolution.
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.