摘要
地震监测是一项非常重要且具有挑战性的任务,遥感技术的不断发展加强了在宏观尺度上对地球表面的监测能力。研究表明,地震前通常都会出现地表温度异常升高的现象,因此各种异常提取算法被应用于地震热异常研究中。其中,基于背景场的提取方法由于具有较强的机理解释性而受到广泛应用。然而,以往基于背景场的异常提取方法更多将背景场限定于某一固定阈值,忽略了受外界因素(非震)影响导致的地表温度的小范围正常波动。据此,文中提出了一种基于GPR-LSTM的地震热红外背景场的构建方法。该方法包括两大部分:震期年变基准场的建立、实际LST的残差波动范围计算及背景场的构建。基于MODIS地表温度产品,以2008年四川汶川和新疆于田地震为研究对象,使用所述方法对地震前兆热异常信息进行提取与分析,经过实验得出以下结论:1)地震热异常通常沿青藏高原的断层分布,这不仅证明了文中方法能够减弱地表温度数据中噪声的干扰,同时也证明该方法在热异常信息提取方面的有效性;2)地震年份的构造活动比非地震年份更加活跃,导致地表温度的异常增温更加明显;3)不同地震案例震前的热异常时空特征各不相同。
Seismic monitoring is a very important and challenging task.The continuous development of remote sensing technology has strengthened our ability to monitor the Earth s surface on a macro scale.Research shows that an abnormal rise in surface temperature usually occurs before an earthquake,so a variety of anomaly extraction algorithms have been applied to the study of seismic thermal anomalies.Among them,the extraction method based on background field is widely used because of its strong mechanism interpretation.However,the previous anomaly methods based on the background field mainly limit the background field to a certain fixed threshold value,and ignore the small range of normal LST fluctuations caused by external factors(non-seismic).Therefore,a method of constructing a seismothermal infrared background field based on GPR-LSTM is proposed in this paper.The main idea of the method is that the LST background field is obtained by adding the established annual variable reference field and the fluctuation range of normal LST.First,the LST exhibits a range of fluctuations due to non-seismic factors such as solar radiation,weather,and human activities.Therefore,it is not reasonable to take a fixed value of the LST background field but it should have a certain fluctuation range.Therefore,the dynamic fluctuation characteristics of the background field should be reflected in the construction process of the background field in this study.Secondly,the reason why this method uses the LSTM model to predict the annual variable reference field of the earthquake period based on the annual variable reference field of the non-earthquake period is that on the one hand,the LSTM model can predict the law of long time series data,so it can better learn the annual variation law of LST in the non-earthquake year.At the same time,the LST trend of increasing or decreasing year by year caused by climate change can be predicted,which is conducive to more truly describing the LST trend of the real background field in the year of the earthqu
作者
宋冬梅
张曼玉
单新建
崔建勇
王斌
SONG Dong-mei;ZHANG Man-yu;SHAN Xin-jian;CUI Jian-yong;WANG Bin(College of Oceanography and spatial information,China University of Petroleum,Qingdao 266580,China;The Laboratory for Marine Mineral Resources,Qingdao National Laboratory for Marine Science and Technology,Qingdao 266071,China;Institute of Geology,China Earthquake Administration,Beijing 100029,China)
出处
《地震地质》
EI
CSCD
北大核心
2024年第2期492-511,共20页
Seismology and Geology
基金
国家重点研发计划项目(2019YFC1509202-4)
国家自然科学基金(U22A20586,41772350,41701513,61371189)
山东省自然科学基金(ZR2022MD015)
山东省重点研发计划项目(2019GGX101033)共同资助。
关键词
背景场
热异常
地震前兆
GPR
LSTM
Background field
Anomaly of heat
Precursor of earthquake
GPR
LSTM
