Document Type
Article
Publication Date
1-27-2020
Abstract
Passive microwave remote sensing technology is an effective means to identify the thermal anomalies associated with earthquakes due to its penetrating capability through clouds compared with infrared sensors. However, observed microwave brightness temperature is strongly influenced by soil moisture and other surface parameters. In the present article, the segmented threshold method has been proposed to detect anomalous microwave brightness temperature associated with the strong earthquakes occurred in Sichuan province, China, an earthquake-prone area with high soil moisture. The index of microwave radiation anomaly (IMRA) computed by the proposed method is found to enhance prior to the three strong earthquakes, 2008 Wenchuan (M = 7.8), 2013 Lushan (M = 6.6), and 2017 Jiuzhaigou (M = 6.5), occurred during 2008-2018 using the Defense Meteorological Space Program Special Sensor Microwave Imager/Sounder F17 satellite data. Our results show that the microwave brightness temperature anomalies appeared about two months prior to the three strong earthquakes. For the Wenchuan and Lushan earthquakes, the enhanced IMRA distributed along the main fault, which is consistent with the variations of our earlier studies of the 1997 Manyi (M = 7.5) and the 2001 Kokoxili (M = 7.8) earthquakes in the region with low soil moisture. For the Jiuzhaigou earthquake, the anomalies distributed around the epicenter and do not indicate the seismogenic structure, which could be due to the presence of a blind fault. It should be noted that quantitative evaluation of IMRA is limited due to infrequent occurrence of earthquakes.
Recommended Citation
F. Jing, R. P. Singh, Y. Cui and K. Sun, "Microwave Brightness Temperature Characteristics of Three Strong Earthquakes in Sichuan Province, China," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 513-522, 2020. https://doi.org/10.1109/JSTARS.2020.2968568
Copyright
The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Included in
Geophysics and Seismology Commons, Remote Sensing Commons, Tectonics and Structure Commons
Comments
This article was originally published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, volume13, in 2020. https://doi.org/10.1109/JSTARS.2020.2968568