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Statistical analysis of pre-seismic anomalies from FY-2G satellite infrared remote sensing images

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China

    University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：1310284@mail.nankai.edu.cn
  • Introduction：乐应波， 研究方向为地震红外遥感研究。E-mail： 1310284@mail.nankai.edu.cn
YUE Yingbo12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China

  • Email：fuchun.chen@mail.sitp.ac.cn
  • Introduction：陈福春， 研究方向为空间光学遥感仪器的研制及遥感仪器定标定量化研究。E-mail： fuchun.chen@mail.sitp.ac.cn
CHEN Fuchun1*,  
  • Affiliation：

    Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China

CHEN Guilin1

resumen

The anomaly from infrared remote sensing images, as an important precursor of earthquakes, is influenced by season changes, weather conditions, and geological and human activities all at the same time, so it needs the stable and effective extraction algorithm to discover an earthquake precursor. The relative change of the power spectrum is a common algorithm used in earthquake case studies to extract information about earthquakes from infrared remote sensing data. However, this algorithm has only been verified in a few earthquakes, and the sample size is considerably small to statistically analyze abnormal signals. In addition, the previous research in statistical analysis involves a small space range, making it impossible to observe the abnormal phenomenon with a large area intuitively and completely.This work proposes a statistical method of pre-seismic infrared anomalies based on connected domain identification to solve the above-mentioned problems. First, abnormal points with the time-space continuity are regarded as an abnormal signal sample. The positive predicted value of the abnormal signals and the true positive rate of earthquakes are calculated with different parameters. The significance test is then carried out in different conditions by using the Molchan diagram method to select the optimal parameters with the largest probability gain. Finally, the accuracy and universality of the algorithm are evaluated by analyzing the peak value and the length of the abnormal signal and relevant seismic information, including the time, magnitude, and location of the epicenter.In this work, this method is applied to the relative power spectrum data of the FY-2G satellite infrared remote sensing images. The data in the long-wave infrared band are used to statistically analyze the pre-seismic infrared anomalies in China and the surrounding areas in 2018. Results show that the positive predictive value of 20.37% and the true positive rate of 65.96% could be achieved, and the probability gain is 1.76. The positive predictive value of the abnormal signals with the high value and the wide area is 80%, and the true positive rate of the earthquakes with a magnitude greater than 5.4 is 81.82%. Meanwhile, the true positive rate has an obvious regional difference, which shows that the true positive rate of earthquakes in the Circum-Pacific seismic zone is higher than that in the Mediterranean-Himalayan zone.The statistical method used in this work has verified that the power spectrum relative change method could extract the infrared abnormal signal before most earthquakes and is more sensitive to earthquakes of magnitude 5.4 and above. The positive predictive value is low, and its application potential is limited. The positive predictive value could be improved to a certain extent by raising the threshold value. This method can be used to analyze the characteristics of abnormal signals and evaluate the correlation between abnormal signals and earthquakes and is beneficial to the comparison and improvement of the algorithm.

palabra clave

remote sensing;FY-2G satellite;infrared remote sensing image;relative power spectrum;pre-earthquake anomaly statistics;connected domain identification

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