An Improved Pattern Informatics Method for Extracting Ionospheric Disturbances Related to Seismicity Based on CSES Data: A Case Study of the Mw7.3 Maduo Earthquake
Abstract The exploration of multi‐layer coupling mechanisms between earthquakes and the ionosphere is crucial for utilizing ionospheric precursors in earthquake prediction. A significant research task involves continuously tracking the spatio‐temporal changes in ionospheric parameters, acquiring com...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
American Geophysical Union (AGU)
2024-12-01
|
| Series: | Earth and Space Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024EA003687 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846110273197309952 |
|---|---|
| author | Weixi Tian Yongxian Zhang Changhui Ju Shengfeng Zhang Maoning Feng Fengli Liu |
| author_facet | Weixi Tian Yongxian Zhang Changhui Ju Shengfeng Zhang Maoning Feng Fengli Liu |
| author_sort | Weixi Tian |
| collection | DOAJ |
| description | Abstract The exploration of multi‐layer coupling mechanisms between earthquakes and the ionosphere is crucial for utilizing ionospheric precursors in earthquake prediction. A significant research task involves continuously tracking the spatio‐temporal changes in ionospheric parameters, acquiring comprehensive seismic anomaly information, and capturing “deterministic” precursor anomalies. Based on data from the China Seismo‐Electromagnetic Satellite (CSES), we enhance the Pattern Informatics (PI) Method and propose an Improved Pattern Informatics (IPI) Method. The IPI method enables the calculation of the spatio‐temporal dynamics of electron density anomalies detected by the CSES satellite. The seismic signals in the electron density during earthquake on 2021 at Maduo are investigated in this work. The results show that: (a) Compared to original electron density images, the IPI method‐derived models extract distinct electron density anomaly signals, regardless of the data whether are collected during descending (daytime) or ascending (nighttime) orbits, or across different time scales of change window. (b) The electron density anomalies appear about 40 days prior to the Maduo Mw7.3 earthquake. The evolution of these anomalies follows a pattern of appearance, persistence, disappearance, re‐emergence, and final disappearance. Moreover, the evolution trends of the IPI hotspot images at daytime and nighttime are similar. These results suggest that the IPI method can capture the spatio‐temporal trends of ionospheric parameters and effectively extract electronic precursors related to strong earthquakes. |
| format | Article |
| id | doaj-art-ba8294ce2c504aecbdad713f87b3cd72 |
| institution | Kabale University |
| issn | 2333-5084 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Earth and Space Science |
| spelling | doaj-art-ba8294ce2c504aecbdad713f87b3cd722024-12-24T13:18:30ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842024-12-011112n/an/a10.1029/2024EA003687An Improved Pattern Informatics Method for Extracting Ionospheric Disturbances Related to Seismicity Based on CSES Data: A Case Study of the Mw7.3 Maduo EarthquakeWeixi Tian0Yongxian Zhang1Changhui Ju2Shengfeng Zhang3Maoning Feng4Fengli Liu5Institute of Geophysics China Earthquake Administration Beijing ChinaInstitute of Earthquake Forecasting China Earthquake Administration Beijing ChinaInstitute of Earthquake Forecasting China Earthquake Administration Beijing ChinaInstitute of Earthquake Forecasting China Earthquake Administration Beijing ChinaInstitute of Earthquake Forecasting China Earthquake Administration Beijing ChinaDepartment of Earth and Space Sciences Southern University of Science and Technology Shenzhen ChinaAbstract The exploration of multi‐layer coupling mechanisms between earthquakes and the ionosphere is crucial for utilizing ionospheric precursors in earthquake prediction. A significant research task involves continuously tracking the spatio‐temporal changes in ionospheric parameters, acquiring comprehensive seismic anomaly information, and capturing “deterministic” precursor anomalies. Based on data from the China Seismo‐Electromagnetic Satellite (CSES), we enhance the Pattern Informatics (PI) Method and propose an Improved Pattern Informatics (IPI) Method. The IPI method enables the calculation of the spatio‐temporal dynamics of electron density anomalies detected by the CSES satellite. The seismic signals in the electron density during earthquake on 2021 at Maduo are investigated in this work. The results show that: (a) Compared to original electron density images, the IPI method‐derived models extract distinct electron density anomaly signals, regardless of the data whether are collected during descending (daytime) or ascending (nighttime) orbits, or across different time scales of change window. (b) The electron density anomalies appear about 40 days prior to the Maduo Mw7.3 earthquake. The evolution of these anomalies follows a pattern of appearance, persistence, disappearance, re‐emergence, and final disappearance. Moreover, the evolution trends of the IPI hotspot images at daytime and nighttime are similar. These results suggest that the IPI method can capture the spatio‐temporal trends of ionospheric parameters and effectively extract electronic precursors related to strong earthquakes.https://doi.org/10.1029/2024EA003687improved pattern informatics method (IPI method)China seismo‐electromagnetic satellite (CSES)seismo‐ionospheric disturbancesMw 7.3 Maduo earthquakeearthquake prediction |
| spellingShingle | Weixi Tian Yongxian Zhang Changhui Ju Shengfeng Zhang Maoning Feng Fengli Liu An Improved Pattern Informatics Method for Extracting Ionospheric Disturbances Related to Seismicity Based on CSES Data: A Case Study of the Mw7.3 Maduo Earthquake Earth and Space Science improved pattern informatics method (IPI method) China seismo‐electromagnetic satellite (CSES) seismo‐ionospheric disturbances Mw 7.3 Maduo earthquake earthquake prediction |
| title | An Improved Pattern Informatics Method for Extracting Ionospheric Disturbances Related to Seismicity Based on CSES Data: A Case Study of the Mw7.3 Maduo Earthquake |
| title_full | An Improved Pattern Informatics Method for Extracting Ionospheric Disturbances Related to Seismicity Based on CSES Data: A Case Study of the Mw7.3 Maduo Earthquake |
| title_fullStr | An Improved Pattern Informatics Method for Extracting Ionospheric Disturbances Related to Seismicity Based on CSES Data: A Case Study of the Mw7.3 Maduo Earthquake |
| title_full_unstemmed | An Improved Pattern Informatics Method for Extracting Ionospheric Disturbances Related to Seismicity Based on CSES Data: A Case Study of the Mw7.3 Maduo Earthquake |
| title_short | An Improved Pattern Informatics Method for Extracting Ionospheric Disturbances Related to Seismicity Based on CSES Data: A Case Study of the Mw7.3 Maduo Earthquake |
| title_sort | improved pattern informatics method for extracting ionospheric disturbances related to seismicity based on cses data a case study of the mw7 3 maduo earthquake |
| topic | improved pattern informatics method (IPI method) China seismo‐electromagnetic satellite (CSES) seismo‐ionospheric disturbances Mw 7.3 Maduo earthquake earthquake prediction |
| url | https://doi.org/10.1029/2024EA003687 |
| work_keys_str_mv | AT weixitian animprovedpatterninformaticsmethodforextractingionosphericdisturbancesrelatedtoseismicitybasedoncsesdataacasestudyofthemw73maduoearthquake AT yongxianzhang animprovedpatterninformaticsmethodforextractingionosphericdisturbancesrelatedtoseismicitybasedoncsesdataacasestudyofthemw73maduoearthquake AT changhuiju animprovedpatterninformaticsmethodforextractingionosphericdisturbancesrelatedtoseismicitybasedoncsesdataacasestudyofthemw73maduoearthquake AT shengfengzhang animprovedpatterninformaticsmethodforextractingionosphericdisturbancesrelatedtoseismicitybasedoncsesdataacasestudyofthemw73maduoearthquake AT maoningfeng animprovedpatterninformaticsmethodforextractingionosphericdisturbancesrelatedtoseismicitybasedoncsesdataacasestudyofthemw73maduoearthquake AT fengliliu animprovedpatterninformaticsmethodforextractingionosphericdisturbancesrelatedtoseismicitybasedoncsesdataacasestudyofthemw73maduoearthquake AT weixitian improvedpatterninformaticsmethodforextractingionosphericdisturbancesrelatedtoseismicitybasedoncsesdataacasestudyofthemw73maduoearthquake AT yongxianzhang improvedpatterninformaticsmethodforextractingionosphericdisturbancesrelatedtoseismicitybasedoncsesdataacasestudyofthemw73maduoearthquake AT changhuiju improvedpatterninformaticsmethodforextractingionosphericdisturbancesrelatedtoseismicitybasedoncsesdataacasestudyofthemw73maduoearthquake AT shengfengzhang improvedpatterninformaticsmethodforextractingionosphericdisturbancesrelatedtoseismicitybasedoncsesdataacasestudyofthemw73maduoearthquake AT maoningfeng improvedpatterninformaticsmethodforextractingionosphericdisturbancesrelatedtoseismicitybasedoncsesdataacasestudyofthemw73maduoearthquake AT fengliliu improvedpatterninformaticsmethodforextractingionosphericdisturbancesrelatedtoseismicitybasedoncsesdataacasestudyofthemw73maduoearthquake |