Pronounced Changes in Thermal Signals Associated with the Madoi (China) M 7.3 Earthquake from Passive Microwave and Infrared Satellite Data

Authors

Feng Jing, China Earthquake AdministrationFollow
Lu Zhang, China Earthquake Administration
Ramesh P. Singh, Chapman UniversityFollow

Document Type

Article

Publication Date

5-26-2022

Abstract

Thermal variations in surface and atmosphere observed from multiple satellites prior to strong earthquakes have been widely reported ever since seismic thermal anomalies were discovered three decades ago. These thermal changes are related to stress accumulation caused by the tectonic activities in the final stage of earthquake preparation. In the present paper, we focused on the thermal changes associated with the 2021 Madoi M 7.3 earthquake in China and analyzed the temporal and spatial evolution of the Index of Microwave Radiation Anomaly (IMRA) and the Index of Longwave Radiation Anomaly (ILRA) based on 8-year microwave brightness temperature (MWBT) and 14-year outgoing longwave radiation (OLR) data collected by satellites. We also explored their responses in different tectonic units (seismogenic fault zone and active tectonic block). Our results indicated that the enhanced IMRA was distributed along the seismogenic fault since mid-February and reappeared for a longer time and with stronger intensity in March and April 2021. The pronounced enhancement in the ILRA was observed within one month over Bayan Har tectonic and adjacent blocks. The higher ILRA over the tectonic blocks in the southern Tibet Plateau at the beginning of 2021 could be associated with the regional stress accumulation, as proven by the occurrences of two moderate earthquakes during this period.

Comments

This article was originally published in Remote Sensing, volume 14, issue 11, in 2022. https://doi.org/10.3390/rs14112539

Recommended Citation

Jing, F.; Zhang, L.; Singh, R.P. Pronounced Changes in Thermal Signals Associated with the Madoi (China) M 7.3 Earthquake from Passive Microwave and Infrared Satellite Data. Remote Sens. 2022, 14, 2539. https://doi.org/10.3390/rs14112539

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