Paper Info
Title
Spatio-Temporally Weighted Two-Step Method for Retrieving Seismic MBT Anomaly: May 2008 Wenchuan Earthquake Sequence Being a Case
Abstract
Referring to the first law of geography and its extension in time domain, this research makes improvements on the two-step method (TSM) presented to retrieve seismic microwave brightness temperature (MBT) anomaly, and a new method named spatio-temporally weighted TSM (STW-TSM) is developed. Temporally weighted background is calculated using nonseismic data according to respective time intervals with the shocking year, and applied to remove long-term trend so as to retrieve basic MBT residuals. Spatially weighted background is calculated referring to Euclidean geospatial distance away from the epicenter, and applied to eliminate local meteorological noise so as to retrieve cleaned MBT residuals. With May 2008 Wenchuan earthquake sequence being a case study, significant seismic MBT anomalies at H and V polarizations, 10 days before and after the May 12 Mw7.9 main shock, were retrieved using STW-TSM at 6.9, 10.7, 18.7, and 36.5 GHz, respectively. This study shows that the newly developed STW-TSM is much more qualified. More significant seismic MBT anomalies at H polarization was retrieved as compared to V polarization. More detailed and localized MBT anomalies was uncovered from high frequency bands at 18.7 and 36.5 GHz, while much remarkable anomalies (but less details) were obtained from low frequency bands at 6.9 and 10.7 GHz. This research is valuable for satellite MBT observation, seismic anomaly analysis, and earthquake precursor study.
Year
DOI
Venue
2020
10.1109/JSTARS.2019.2962719
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Keywords
DocType
Volume
Advanced Microwave Scanning Radiometer for the Earth Observing System,microwave brightness temperature (MBT),seismic anomaly,spatio-temporally weighted,two-step method (TSM)
Journal
13
ISSN
Citations 
PageRank 
1939-1404
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Yuan Qi102.37
Lixin Wu29435.60
Miao He300.34
Wenfei Mao412.72