地震构造区红外亮温背景场建立及异常提取方法研究
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摘要
我国地震活动具有分布广、频度高、强度大、震源浅、灾害重的特点,是地震灾害最为严重的国家之一。因此,准确的地震预报,尤其短临预报是减轻地震灾害的重要前提。地震前会出现近地表温度异常早已被国内外地震学者所关注,近年来大量的研究工作表明,利用卫星热红外遥感探测构造活动及地震活动引起的红外辐射热异常已成为地震研究领域具有广阔应用前景的技术手段。
在卫星热红外异常信息提取方面,国内外很多学者都是利用地面增温来提取震前红外异常。卫星红外亮温就是利用星载或机载传感器收集、记录来自地球表面或低空大气的热辐射信息数据,然后根据黑体辐射定律计算得到的。但是,卫星红外亮温是地表、大气诸多因素热辐射的综合反映,它受到地形地貌、地表覆盖类型和气象等非构造因素的综合影响,在平静(无震)时期亮温的分布也是不均匀的,仅依据遥感图像上的亮温值大小来判别异常是不合理的。异常的识别和提取是建立在正常动态背景的基础上的,因此,研究正常情况下亮温背景场的时空演化规律是异常判定的前提。
本文利用NOAA/AVHRR极轨气象卫星热红外遥感影像,选取2个地震重点监测区为研究对象,建立了不同时间尺度上的红外亮温背景场,同时分析了实验区红外亮温背景场的时空演化特征及变化规律,为有效地挖掘与地震相关的热红外异常信息奠定基础;针对NOAA/AVHRR卫星数据,在亮温动态背景场分析研究的基础上,设计提出了两种亮温异常检测和提取的算法模型:一种是基于历年同期亮温偏移指数法,一种是改进的RST方法。此外,通过分析卫星观测亮温与野外观测数据的相关性和差异性,验证红外亮温用于震前异常信息提取的可靠性。
研究结果表明:(1)红外亮温背景场建立方面:空间上,亮温变化主要受地形地貌和断裂构造的控制,在不考虑城市热岛效应情况下,地面高程每增加100m亮温降低约0.56℃～0.68℃,与大气温度直减率基本一致;时间上,实验区亮温均值变化主要受季节规律的影响,表现出明显的夏高冬低的特征,夏季最高温约10℃,冬季最低温约-18℃。不同地质构造单元,尤其线性断裂构造及水系在热红外影像上有清晰的表现。总的来讲,热红外亮温背景场变化具有空间相关性、时间延续性和年变周期性,基于多年积累数据的统计分析可以有效提取红外亮温异常;(2)异常提取算法模型:为了有效地进行红外亮温异常的提取,设计了基于历年同期亮温偏移指数K值算法模型,并对RST算法进行改进,为红外亮温异常的提取提供技术支撑;(3)红外亮温相关性分析:亮温与气温及0.2m地温呈现很好的相关性,相关系数分别为0.92、0.93,三者变化具有同步变化趋势。这说明亮温是对地表辐射状态的良好反应,用亮温研究地震异常是可靠的,可以为地震预测预报提供基础服务。(4)红外亮温异常提取方面:通过K值和改进的RST算法在震例中的应用,总结了地震红外亮温异常的时空演化规律:时间上,异常出现的时间多在震前数日至数月;空间上,异常的表现形式为由弱-强-弱-消失的过程,异常形态多为团块状或与断裂带走向较为一致的条带状。(5)异常提取算法对比:通过K值和改进的RST这2种算法在玉树地震中的应用,结果表明改进RST算法具有对异常的反应程度大、细节清晰且异常范围大并可以扣除气象因素等引起的气温突变等优点。
The seismic activities in China the has the characters of wide distribution,high frequency,big strength,shallow quakes,heavy disasters. China is one of the countries hit most seriously by earthquake in the world. Therefore ,accurate earthquake prediction,especially short-term and impending prediction is the important prerequisite of reducing earthquake disaster. The near-surface temperature anomalies prior to earthquake have been concerned by the scholars at home and abroad,who have worked a lot in this field. The research shows thermal infrared remote sensing which is used to detect infrared radiation anomalies caused by tectonic activities or seismic activities,has the widest application in the earthquake research field.
On the part of extracting thermal infrared anomalies,many researchers extract infrared anomalies before earthquakes by increased brightness temperature. The satellite infrared brightness temperature is calculated according to the black body radiation law using the object thermal radiation intensity data collected and recorded by on-board sensors from surface or low altitude atmosphere. But infrared brightness temperature is the comprehensive reflection of thermal radiation of surface and atmosphere. The distribution of brightness temperature is uneven in normal situation because it is influenced by non-structural factors such as topography and geomorphology ,geophysics cover type,meteorological factor,and so on. It is unreasonable to determine whether anomalies exist or not according to the level of the brightness temperature on remote sensing images. Anomalies judgment and extraction must be on the base of understanding of the normal dynamic background. Therefore it is necessary to study the brightness temperature time-space evolution rules in normal situations,that is the premise of anomalies judgment.
This paper establishes the brightness temperature background field using NOAA/AVHRR polar orbit meteorological satellite thermal infrared remote sense data in different time scales and analyses the time-space evolution features and rules of infrared brightness temperature background field at the same time,which is aimed at laying the foundations for mining thermal infrared anomalies information related to earthquake effectively. According to NOAA/AVHRR satellite data,the paper designs two brightness temperature anomalies detection and extraction on the basis of analyzing brightness temperature background field:one is brightness temperature offset index based on the same calendar year,the other is improved RST method. Moreover,we confirm the availability of extracting anomalies information using brightness temperature through the analysis of the correlation and difference between satellite observation data and field observation data.
The findings indicate:(1)With respect to the foundation of infrared brightness temperature background field,the variation of brightness temperature is mainly controlled by landform and faults in space;the variation that shows the obvious characteristics of peaking in summer and low in winter is influenced by seasonal rules in time. The maximum temperature of study zone in summer is 10℃,the minimum temperature of study zone in winter is -18℃. Different geological tectonic units,especially linear active faults and water system display clearly on the thermal infrared brightness temperature images. On the whole,thermal infrared brightness temperature background field has the characteristics of spatial correlation、temporal continuity and annual variation period. We can effectively extract infrared brightness temperature anomalies based on statistic analysis of years of accumulation data;(2)The algorithm models of anomalies extraction:we design the brightness temperature offset index K based on same calendar year and improve RST,which provide technical support for the anomalies extraction;(3)Through infrared brightness temperature reality testing it can be perceived that brightness temperature shows good correlations with the air temperature and geothermal temperature in the depth of 0.2m , they have the synchronous variation tendency. This suggests that it is reliable to study earthquake anomalies using brightness temperature which is the good response of surface radiation conditions. The brightness temperature can provide the basis service for earthquake forecast.(4)At the aspect of infrared brightness temperature anomalies,the paper concludes time-space evolution rules of anomalies through studying seismic cases:the time of anomalies emergence is from days to months in time;the manifestations of anomalies is the process of weak-strong-weak-disappear,the morphology of anomalies is most cloddy or stripped which is consistent with the fault.(5)The contrast of the algorithm models of anomalies extraction:We study the earthquake of Yushu using the K-value and improved RST. The results show improved RST has the characters of sensitive reaction on anomalies,distinct details and the bigger area of anomalies,moreover,it has the advantage of deducting the temperature mutations which is caused by meteorological factors,and so on.
在卫星热红外异常信息提取方面,国内外很多学者都是利用地面增温来提取震前红外异常。卫星红外亮温就是利用星载或机载传感器收集、记录来自地球表面或低空大气的热辐射信息数据,然后根据黑体辐射定律计算得到的。但是,卫星红外亮温是地表、大气诸多因素热辐射的综合反映,它受到地形地貌、地表覆盖类型和气象等非构造因素的综合影响,在平静(无震)时期亮温的分布也是不均匀的,仅依据遥感图像上的亮温值大小来判别异常是不合理的。异常的识别和提取是建立在正常动态背景的基础上的,因此,研究正常情况下亮温背景场的时空演化规律是异常判定的前提。
本文利用NOAA/AVHRR极轨气象卫星热红外遥感影像,选取2个地震重点监测区为研究对象,建立了不同时间尺度上的红外亮温背景场,同时分析了实验区红外亮温背景场的时空演化特征及变化规律,为有效地挖掘与地震相关的热红外异常信息奠定基础;针对NOAA/AVHRR卫星数据,在亮温动态背景场分析研究的基础上,设计提出了两种亮温异常检测和提取的算法模型:一种是基于历年同期亮温偏移指数法,一种是改进的RST方法。此外,通过分析卫星观测亮温与野外观测数据的相关性和差异性,验证红外亮温用于震前异常信息提取的可靠性。
研究结果表明:(1)红外亮温背景场建立方面:空间上,亮温变化主要受地形地貌和断裂构造的控制,在不考虑城市热岛效应情况下,地面高程每增加100m亮温降低约0.56℃～0.68℃,与大气温度直减率基本一致;时间上,实验区亮温均值变化主要受季节规律的影响,表现出明显的夏高冬低的特征,夏季最高温约10℃,冬季最低温约-18℃。不同地质构造单元,尤其线性断裂构造及水系在热红外影像上有清晰的表现。总的来讲,热红外亮温背景场变化具有空间相关性、时间延续性和年变周期性,基于多年积累数据的统计分析可以有效提取红外亮温异常;(2)异常提取算法模型:为了有效地进行红外亮温异常的提取,设计了基于历年同期亮温偏移指数K值算法模型,并对RST算法进行改进,为红外亮温异常的提取提供技术支撑;(3)红外亮温相关性分析:亮温与气温及0.2m地温呈现很好的相关性,相关系数分别为0.92、0.93,三者变化具有同步变化趋势。这说明亮温是对地表辐射状态的良好反应,用亮温研究地震异常是可靠的,可以为地震预测预报提供基础服务。(4)红外亮温异常提取方面:通过K值和改进的RST算法在震例中的应用,总结了地震红外亮温异常的时空演化规律:时间上,异常出现的时间多在震前数日至数月;空间上,异常的表现形式为由弱-强-弱-消失的过程,异常形态多为团块状或与断裂带走向较为一致的条带状。(5)异常提取算法对比:通过K值和改进的RST这2种算法在玉树地震中的应用,结果表明改进RST算法具有对异常的反应程度大、细节清晰且异常范围大并可以扣除气象因素等引起的气温突变等优点。
The seismic activities in China the has the characters of wide distribution,high frequency,big strength,shallow quakes,heavy disasters. China is one of the countries hit most seriously by earthquake in the world. Therefore ,accurate earthquake prediction,especially short-term and impending prediction is the important prerequisite of reducing earthquake disaster. The near-surface temperature anomalies prior to earthquake have been concerned by the scholars at home and abroad,who have worked a lot in this field. The research shows thermal infrared remote sensing which is used to detect infrared radiation anomalies caused by tectonic activities or seismic activities,has the widest application in the earthquake research field.
On the part of extracting thermal infrared anomalies,many researchers extract infrared anomalies before earthquakes by increased brightness temperature. The satellite infrared brightness temperature is calculated according to the black body radiation law using the object thermal radiation intensity data collected and recorded by on-board sensors from surface or low altitude atmosphere. But infrared brightness temperature is the comprehensive reflection of thermal radiation of surface and atmosphere. The distribution of brightness temperature is uneven in normal situation because it is influenced by non-structural factors such as topography and geomorphology ,geophysics cover type,meteorological factor,and so on. It is unreasonable to determine whether anomalies exist or not according to the level of the brightness temperature on remote sensing images. Anomalies judgment and extraction must be on the base of understanding of the normal dynamic background. Therefore it is necessary to study the brightness temperature time-space evolution rules in normal situations,that is the premise of anomalies judgment.
This paper establishes the brightness temperature background field using NOAA/AVHRR polar orbit meteorological satellite thermal infrared remote sense data in different time scales and analyses the time-space evolution features and rules of infrared brightness temperature background field at the same time,which is aimed at laying the foundations for mining thermal infrared anomalies information related to earthquake effectively. According to NOAA/AVHRR satellite data,the paper designs two brightness temperature anomalies detection and extraction on the basis of analyzing brightness temperature background field:one is brightness temperature offset index based on the same calendar year,the other is improved RST method. Moreover,we confirm the availability of extracting anomalies information using brightness temperature through the analysis of the correlation and difference between satellite observation data and field observation data.
The findings indicate:(1)With respect to the foundation of infrared brightness temperature background field,the variation of brightness temperature is mainly controlled by landform and faults in space;the variation that shows the obvious characteristics of peaking in summer and low in winter is influenced by seasonal rules in time. The maximum temperature of study zone in summer is 10℃,the minimum temperature of study zone in winter is -18℃. Different geological tectonic units,especially linear active faults and water system display clearly on the thermal infrared brightness temperature images. On the whole,thermal infrared brightness temperature background field has the characteristics of spatial correlation、temporal continuity and annual variation period. We can effectively extract infrared brightness temperature anomalies based on statistic analysis of years of accumulation data;(2)The algorithm models of anomalies extraction:we design the brightness temperature offset index K based on same calendar year and improve RST,which provide technical support for the anomalies extraction;(3)Through infrared brightness temperature reality testing it can be perceived that brightness temperature shows good correlations with the air temperature and geothermal temperature in the depth of 0.2m , they have the synchronous variation tendency. This suggests that it is reliable to study earthquake anomalies using brightness temperature which is the good response of surface radiation conditions. The brightness temperature can provide the basis service for earthquake forecast.(4)At the aspect of infrared brightness temperature anomalies,the paper concludes time-space evolution rules of anomalies through studying seismic cases:the time of anomalies emergence is from days to months in time;the manifestations of anomalies is the process of weak-strong-weak-disappear,the morphology of anomalies is most cloddy or stripped which is consistent with the fault.(5)The contrast of the algorithm models of anomalies extraction:We study the earthquake of Yushu using the K-value and improved RST. The results show improved RST has the characters of sensitive reaction on anomalies,distinct details and the bigger area of anomalies,moreover,it has the advantage of deducting the temperature mutations which is caused by meteorological factors,and so on.
引文
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