Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach
Abstract Shear wave splitting (SWS) analysis is widely used to provide critical constraints on crustal and mantle structure and dynamic models. In order to obtain reliable splitting measurements, an essential step is to visually verify all the measurements to reject problematic measurements, a task...
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| Format: | Article |
| Language: | English |
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Wiley
2022-06-01
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| Series: | Geophysical Research Letters |
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| Online Access: | https://doi.org/10.1029/2021GL097101 |
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| author | Yanwei Zhang Stephen S. Gao |
| author_facet | Yanwei Zhang Stephen S. Gao |
| author_sort | Yanwei Zhang |
| collection | DOAJ |
| description | Abstract Shear wave splitting (SWS) analysis is widely used to provide critical constraints on crustal and mantle structure and dynamic models. In order to obtain reliable splitting measurements, an essential step is to visually verify all the measurements to reject problematic measurements, a task that is increasingly time consuming due to the exponential increase in the amount of data. In this study, we utilized a convolutional neural network (CNN) based method to automatically select reliable SWS measurements. The CNN was trained by human‐verified teleseismic SWS measurements and tested using synthetic SWS measurements. Application of the trained CNN to broadband seismic data recorded in south central Alaska reveals that CNN classifies 97.0% of human selected measurements as acceptable, and revealed ∼30% additional measurements. To our knowledge, this is the first study to systematically explore the potential of a machine‐learning based technique to assist with SWS analysis. |
| format | Article |
| id | doaj-art-d5a6bf6824934cb7a6fcfb5a9c5b0069 |
| institution | Kabale University |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-d5a6bf6824934cb7a6fcfb5a9c5b00692025-01-22T14:38:16ZengWileyGeophysical Research Letters0094-82761944-80072022-06-014912n/an/a10.1029/2021GL097101Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network ApproachYanwei Zhang0Stephen S. Gao1Geology and Geophysics Program Missouri University of Science and Technology Rolla MO USAGeology and Geophysics Program Missouri University of Science and Technology Rolla MO USAAbstract Shear wave splitting (SWS) analysis is widely used to provide critical constraints on crustal and mantle structure and dynamic models. In order to obtain reliable splitting measurements, an essential step is to visually verify all the measurements to reject problematic measurements, a task that is increasingly time consuming due to the exponential increase in the amount of data. In this study, we utilized a convolutional neural network (CNN) based method to automatically select reliable SWS measurements. The CNN was trained by human‐verified teleseismic SWS measurements and tested using synthetic SWS measurements. Application of the trained CNN to broadband seismic data recorded in south central Alaska reveals that CNN classifies 97.0% of human selected measurements as acceptable, and revealed ∼30% additional measurements. To our knowledge, this is the first study to systematically explore the potential of a machine‐learning based technique to assist with SWS analysis.https://doi.org/10.1029/2021GL097101shear wave splittingseismic anisotropymachine learningdata miningconvolutional neural network |
| spellingShingle | Yanwei Zhang Stephen S. Gao Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach Geophysical Research Letters shear wave splitting seismic anisotropy machine learning data mining convolutional neural network |
| title | Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach |
| title_full | Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach |
| title_fullStr | Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach |
| title_full_unstemmed | Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach |
| title_short | Classification of Teleseismic Shear Wave Splitting Measurements: A Convolutional Neural Network Approach |
| title_sort | classification of teleseismic shear wave splitting measurements a convolutional neural network approach |
| topic | shear wave splitting seismic anisotropy machine learning data mining convolutional neural network |
| url | https://doi.org/10.1029/2021GL097101 |
| work_keys_str_mv | AT yanweizhang classificationofteleseismicshearwavesplittingmeasurementsaconvolutionalneuralnetworkapproach AT stephensgao classificationofteleseismicshearwavesplittingmeasurementsaconvolutionalneuralnetworkapproach |