Identification of Rare Multiple Core‐Mantle Boundary Reflections PmKP Up To P7KP With Deep Learning
Abstract The core‐mantle boundary (CMB) marks the most dramatic changes in physical properties within the Earth, and plays a critical role in the understanding of the Earth's dynamics. PmKP waves are seismic phases that reflect (m − 1) times under the CMB and are useful for studying the complex...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Geophysical Research Letters |
| Online Access: | https://doi.org/10.1029/2023GL105464 |
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| _version_ | 1849723354504757248 |
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| author | Sheng Dong Yulin Chen Baolong Zhang Sidao Ni Xiaofei Chen Yi Wang |
| author_facet | Sheng Dong Yulin Chen Baolong Zhang Sidao Ni Xiaofei Chen Yi Wang |
| author_sort | Sheng Dong |
| collection | DOAJ |
| description | Abstract The core‐mantle boundary (CMB) marks the most dramatic changes in physical properties within the Earth, and plays a critical role in the understanding of the Earth's dynamics. PmKP waves are seismic phases that reflect (m − 1) times under the CMB and are useful for studying the complex CMB structure. We present an automated workflow for detecting PmKP phases using multi‐station records from global seismic stations. We employ a novel sampling method to extract PmKP waveforms into a 2‐D matrix. Two deep neural networks are then utilized for initial phase detections and subsequent slowness validations. Numerous PmKPab (3 ≤ m ≤ 7) and their CMB diffracted signals were identified for deep earthquakes (magnitude >6) occurred from 2000 to 2020, including diffracted P7KPab waves with diffraction lengths of nearly 20°. Our approach significantly improves the efficiency of PmKP phase identification and holds the capability to detect other weak core phases, such as PKiKP. |
| format | Article |
| id | doaj-art-feaa5fcd56454d68929fe64ae19af0dd |
| institution | DOAJ |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-feaa5fcd56454d68929fe64ae19af0dd2025-08-20T03:11:03ZengWileyGeophysical Research Letters0094-82761944-80072024-01-01512n/an/a10.1029/2023GL105464Identification of Rare Multiple Core‐Mantle Boundary Reflections PmKP Up To P7KP With Deep LearningSheng Dong0Yulin Chen1Baolong Zhang2Sidao Ni3Xiaofei Chen4Yi Wang5State Key Laboratory of Geodesy and Earth's Dynamics Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan ChinaState Key Laboratory of Geodesy and Earth's Dynamics Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan ChinaState Key Laboratory of Geodesy and Earth's Dynamics Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan ChinaState Key Laboratory of Geodesy and Earth's Dynamics Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan ChinaDepartment of Earth and Space Science Southern University of Science and Technology Shenzhen ChinaState Key Laboratory of Geodesy and Earth's Dynamics Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan ChinaAbstract The core‐mantle boundary (CMB) marks the most dramatic changes in physical properties within the Earth, and plays a critical role in the understanding of the Earth's dynamics. PmKP waves are seismic phases that reflect (m − 1) times under the CMB and are useful for studying the complex CMB structure. We present an automated workflow for detecting PmKP phases using multi‐station records from global seismic stations. We employ a novel sampling method to extract PmKP waveforms into a 2‐D matrix. Two deep neural networks are then utilized for initial phase detections and subsequent slowness validations. Numerous PmKPab (3 ≤ m ≤ 7) and their CMB diffracted signals were identified for deep earthquakes (magnitude >6) occurred from 2000 to 2020, including diffracted P7KPab waves with diffraction lengths of nearly 20°. Our approach significantly improves the efficiency of PmKP phase identification and holds the capability to detect other weak core phases, such as PKiKP.https://doi.org/10.1029/2023GL105464 |
| spellingShingle | Sheng Dong Yulin Chen Baolong Zhang Sidao Ni Xiaofei Chen Yi Wang Identification of Rare Multiple Core‐Mantle Boundary Reflections PmKP Up To P7KP With Deep Learning Geophysical Research Letters |
| title | Identification of Rare Multiple Core‐Mantle Boundary Reflections PmKP Up To P7KP With Deep Learning |
| title_full | Identification of Rare Multiple Core‐Mantle Boundary Reflections PmKP Up To P7KP With Deep Learning |
| title_fullStr | Identification of Rare Multiple Core‐Mantle Boundary Reflections PmKP Up To P7KP With Deep Learning |
| title_full_unstemmed | Identification of Rare Multiple Core‐Mantle Boundary Reflections PmKP Up To P7KP With Deep Learning |
| title_short | Identification of Rare Multiple Core‐Mantle Boundary Reflections PmKP Up To P7KP With Deep Learning |
| title_sort | identification of rare multiple core mantle boundary reflections pmkp up to p7kp with deep learning |
| url | https://doi.org/10.1029/2023GL105464 |
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