Extracting Dispersion Curves From Ambient Noise Correlations Using Deep Learning
We present a machine learning approach to classify the phases of surface wave dispersion curves. Standard frequency-time analysis (FTAN) analysis of seismograms observed on an array of receivers is converted into an image, of which each pixel is classified as fundamental mode, first overtone, or noi...
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| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 2020-12, Vol.58 (12), p.8932-8939 |
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| container_title | IEEE transactions on geoscience and remote sensing |
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| creator | Zhang, Xiaotian Jia, Zhe Ross, Zachary E. Clayton, Robert W. |
| description | We present a machine learning approach to classify the phases of surface wave dispersion curves. Standard frequency-time analysis (FTAN) analysis of seismograms observed on an array of receivers is converted into an image, of which each pixel is classified as fundamental mode, first overtone, or noise. We use a convolutional neural network (U-Net) architecture with a supervised learning objective and incorporate transfer learning. The training is initially performed with synthetic data to learn coarse structure, followed by fine-tuning of the network using approximately 10% of the real data based on human classification. The results show that the machine classification is nearly identical to the human picked phases. Expanding the method to process multiple images at once did not improve the performance. The developed technique will facilitate the automated processing of large dispersion curve data sets. |
| doi_str_mv | 10.1109/TGRS.2020.2992043 |
| format | Article |
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The developed technique will facilitate the automated processing of large dispersion curve data sets.</description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2020.2992043</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Ambient noise ; Artificial neural networks ; Classification ; Convolutional networks ; Correlation ; Data models ; Deep learning ; Dispersion ; Dispersion curve analysis ; dispersion curves ; Frequency analysis ; Learning algorithms ; Machine learning ; Neural networks ; Noise ; Seismograms ; Surface treatment ; Surface water waves ; Surface waves ; Training ; Transfer learning ; Wave dispersion</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2020-12, Vol.58 (12), p.8932-8939</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Standard frequency-time analysis (FTAN) analysis of seismograms observed on an array of receivers is converted into an image, of which each pixel is classified as fundamental mode, first overtone, or noise. We use a convolutional neural network (U-Net) architecture with a supervised learning objective and incorporate transfer learning. The training is initially performed with synthetic data to learn coarse structure, followed by fine-tuning of the network using approximately 10% of the real data based on human classification. The results show that the machine classification is nearly identical to the human picked phases. Expanding the method to process multiple images at once did not improve the performance. The developed technique will facilitate the automated processing of large dispersion curve data sets.</description><subject>Ambient noise</subject><subject>Artificial neural networks</subject><subject>Classification</subject><subject>Convolutional networks</subject><subject>Correlation</subject><subject>Data models</subject><subject>Deep learning</subject><subject>Dispersion</subject><subject>Dispersion curve analysis</subject><subject>dispersion curves</subject><subject>Frequency analysis</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Noise</subject><subject>Seismograms</subject><subject>Surface treatment</subject><subject>Surface water waves</subject><subject>Surface waves</subject><subject>Training</subject><subject>Transfer learning</subject><subject>Wave dispersion</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQhoMoWKs_QLwEPG-dfJocy9pWoahoew7Z7axsaXfXZCv6701t8TQM87wzw0PINYMRY2DvFrO39xEHDiNuLQcpTsiAKWUy0FKekgEwqzNuLD8nFzGuAZhU7H5AXiffffBlXzcf9KGOHYZYtw3Nd-ELI52GdkvH26LGpqfPbR2R5m0IuPF9oiJdxr8cYkfn6EOTuktyVvlNxKtjHZLldLLIH7P5y-wpH8-zUgjdZ1pYj6oolDQAK8-9KIxRaAS3FYDlyqS_NfeIkrOVZ0pzoVRZclYqVWEhhuT2sLcL7ecOY-_W7S406aTjUispNGMmUexAlaGNMWDlulBvffhxDNxenNuLc3tx7iguZW4OmRoR_3kLaayt-AXPVmjh</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Zhang, Xiaotian</creator><creator>Jia, Zhe</creator><creator>Ross, Zachary E.</creator><creator>Clayton, Robert W.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Ambient noise Artificial neural networks Classification Convolutional networks Correlation Data models Deep learning Dispersion Dispersion curve analysis dispersion curves Frequency analysis Learning algorithms Machine learning Neural networks Noise Seismograms Surface treatment Surface water waves Surface waves Training Transfer learning Wave dispersion |
| title | Extracting Dispersion Curves From Ambient Noise Correlations Using Deep Learning |
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