Detecting Seismic Anomalies in Outgoing Long-Wave Radiation Data

Xiangzeng Kong, Yaxin Bi, David Glass

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


In this paper, we propose a Geometric Moving Average Martingale (GMAM) method for change detection. There are two components underpinning the method which enable it to reduce false detections. The first is the exponential weighting of observations to obtain the GMAM value and the second is the use of the value for hypothesis testing to determine whether a change has occurred. Extension of the GMAM method to the average GMAM (AG) method has been applied to analyze seismic anomalies within outgoing long-wave radiation (OLR) data observed by satellites from 2006 to 2013 for the two recent Wenchuan and Lushan earthquakes and four comparative study areas: Wenchuan, Puer, Beijing, and Northeastern areas. The Yushu earthquake and Hetian earthquake have also been examined. The experimental results show that the proposed AG method can effectively extract abnormal changes within OLR data and that there are large AG values in the pre and post occurrence of the earthquakes in these areas, which could be viewed as seismic anomalies, and the AG method has experimentally compared with the deviation method. The experimental results show that the AG method can effectively reflect the change process in OLR data.
Original languageEnglish
Pages (from-to)649-660
Number of pages12
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number2
Early online date19 Nov 2014
Publication statusPublished (in print/issue) - 9 Feb 2015


  • Anomaly detection
  • Geometric Moving Average Martingale
  • outgoing long-wave radiation


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