Statistical analysis of pre-seismic anomalies from FY-2G satellite infrared remote sensing images
- Vol. 26, Issue 12, Pages: 2655-2664(2022)
Received：29 July 2020，
Published：07 December 2022
DOI： 10.11834/jrs.20210251
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摘要
红外遥感图像异常是一种重要的地震前兆，需要稳定且有效的提取算法才能发现地震前兆。然而，许多算法只是在少数地震中有验证，数据样本少，不能进行异常信号的统计分析。本文提出了一种方法，对中国及周边地区的红外遥感图像的功率谱异常信号进行统计，以评估算法的准确性和普适性。该方法还分析异常信号的幅度、空间范围和相关的地震信息，将时间和空间上连续的异常点视为一个样本，以计算不同参数条件下异常信号的阳性预测值和地震的真阳性率。本文提取了FY-2G卫星的红外遥感图像中的地震异常信号，并进行统计，得到20.37%的阳性预测值和65.96%的真阳性率。高幅度大范围的异常信号可以达到80%的阳性预测值。对于大于5.4级的地震，真阳性率可以达到81.82%。本文验证了功率谱相对变化法能在大部分地震前提取到红外遥感图像异常，该方法可以分析异常信号的特征和评估异常信号与地震的相关性，有利于算法的对比和改进。
Abstract
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.
关键词
Keywords
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YANG Yunjie 西南交通大学 地球科学与工程学院
ZHANG Rui 西南交通大学 地球科学与工程学院
JIANG Han 西南交通大学 地球科学与工程学院
ZHANG Bo 西南交通大学 地球科学与工程学院
SONG Yunfan 中国气象局成都高原气象研究所
LIU Guoxiang 西南交通大学 地球科学与工程学院
SHI Yiwen 上海师范大学 环境与地理科学学院
YU Qinping 上海师范大学 环境与地理科学学院
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Faculty of Geosciences and Engineering, Southwest Jiaotong University
Chengdu Institute of Plateau Meteorology, China Meteorological Administration
School of Environmental and Geographical Sciences, Shanghai Normal University
Yangtze River Delta Urban Wetland Ecosystem National Field Scientific Observation and Research Station
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences
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