Document Type
Article
Publication Date
8-14-2023
Abstract
Over the past three decades, thermal anomalies from satellite infrared data have been widely discussed and associated with earthquakes. Besides infrared satellite data, the recent use of passive microwave satellite data has emerged as an important dataset to detect the potential thermal anomalies prior to earthquakes in all weather conditions due to its capability to monitor the earth in cloudy and foggy conditions and has been applied to the numerous strong earthquake events around the world. On 5 September 2022, an Mw 6.6 earthquake occurred in Luding county, nearby Mt. Gongga, China, with heavy cloud coverage over the epicenter and its surrounding region before and after the event. In the present study, the microwave brightness temperature (MWBT) and clear-sky outgoing longwave radiation (ClrOLR) data were used together to identify the potential seismic thermal anomalies. Prominent anomalous signals were detected around the epicenter, appearing within 2 months preceding the earthquake. Further analysis was conducted through the combination of other parameters from satellite and ground observations, including precipitation, cloud fraction, and soil moisture, in order to verify and better understand the detected anomalous signals. Our results suggest that the combination of MWBT and ClrOLR can be used as an effective tool to detect thermal anomalies associated with earthquake events in cloudy conditions over the epicenter.
Recommended Citation
F. Jing, M. Jiang, L. Zhang and R. P. Singh, "Detection and Identification of Pre-Seismic Thermal Anomalies in cloudy conditions associated with the 2022 Luding (China) Mw 6.6 Earthquake," in IEEE Transactions on Geoscience and Remote Sensing, https://doi.org/10.1109/TGRS.2023.3304975.
Copyright
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Included in
Environmental Indicators and Impact Assessment Commons, Environmental Monitoring Commons, Geophysics and Seismology Commons
Comments
This is a pre-copy-editing, author-produced PDF of an article accepted for publication in IEEE Transactions on Geoscience and Remote Sensing in 2023 following peer review. This article may not exactly replicate the final published version. The definitive publisher-authenticated version is available online at https://doi.org/10.1109/TGRS.2023.3304975.