基于3S技术强震区地质灾害解译与危险性评价研究
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摘要
2008年5月12日14时28分,四川省汶川县映秀镇发生里氏8.0级强烈地震,地震波及大半个中国,受灾面积达10万平方公里。汶川大地震触发了大量的崩塌滑坡地质灾害,据不完全统计,地震触发的地质灾害点2万余处,总量估计在5万处以上。其中常见的四种地质灾害崩塌、滑坡、泥石流及不稳定斜坡占总数的97%以上。地震是诱发滑坡次生灾害的动力成因之一,每当强烈地震发生后,斜坡产生变形破坏,导致大量的滑坡产生,这些滑坡称之为“地震滑坡”。强地震作用将长期影响着斜坡的稳定性,特别是在雨季,地质灾害连绵不断,泥石流转为旺盛,主要原因是暴雨也引起大量的滑坡产生,这些滑坡称之为“暴雨滑坡”,因此暴雨也是诱发地质灾害的动力成因之一。
目前对地震滑坡的研究主要集中于同发型地震滑坡的识别和特征研究,然而对震后降雨诱发的暴雨滑坡活动特征的动态分析较少,开展地震滑坡和暴雨滑坡两者之间产生的滑坡数量、面积、滑坡特征研究就更少了。鉴于目前国内尚未有针对强震区地震滑坡和暴雨滑坡进行动态对比分析的研究成果,论文基于地质学、地貌学、3S技术等相关学科理论及国内外研究进展及动态的基础上,通过对“5.12”汶川大地震强震区之一的北川县大量产生的地质灾害的野外调查基础之上,利用多时相、高分辨率的航片、P5、SPOT5卫星图像资料对北川全县进行地质灾害点状遥感解译以及对北川县典型区进行地质灾害面状遥感解译,建立强震区地质灾害遥感解译的判识特征,开展强震区北川县典型区的地质灾害动态分析,重点分析对比地震滑坡和暴雨滑坡数量类型特征,同时在完成对北川全县地质灾害点状解译的基础之上,开展强震区北川县地质灾害危险性评价方法与体系的研究,并建立强震区地质灾害危险性评价模式,可以快速有效的展开强震区地质灾害的快速评价。
论文主要得到以下研究成果:
(1)初步建立了较为系统的利用高精度遥感技术研究地质灾害的方法体系,结合崩滑流地质灾害的成因及图像形态特征(形态、色调、阴影、纹理等)修正和完善了地质灾害遥感解译的判别标志和解译方法,阐明了遥感技术在地质灾害研究中的优点和局限性。
(2)利用多时相、高分辨率的航片、P5、SPOT5卫星图像,完成对北川县典型区进行了5.12地震之前、5.12地震之后、9.24暴雨之后的单体滑坡、崩塌、泥石流和区域地质灾害数量和面积规模特征对比动态分析,表明:地震是诱发滑坡次生灾害的动力成因之一,每当强烈地震发生后,斜坡产生变形破坏,导致大面积滑坡;同时暴雨也是诱发滑坡次生灾害的动力成因之一,且暴雨诱发的地质灾害往往具有区域性、群发性、同时性、暴发性和成灾大的特点,结合两期影像对比9.24暴雨诱发的地质灾害体面积是5.12地震直接诱发滑坡面积的1/4倍,随着时间的推移和降雨的发生,降雨引起的滑坡面积将持续增加;暴雨不仅诱发新的滑坡,而且促使原来地震滑坡复活,并扩大其面积,暴雨后地震滑坡面积扩大了原来面积的1/8倍,同样随着时间的推移和降雨的发生,地震滑坡面积将持续扩大。
(3)引入地质灾害的空间概率和时间概率概念,完善了地质灾害危险性概念。重点分析了地震诱发地质灾害分布与评价指标的空间分布量化关系和内在本质联系,并结合降雨诱发地质灾害因子进行了地质灾害危险性评价体系的构建。结果表明:高危险区一般与地质灾害分布密度有较好的对应关系,评价表明尽管高危险区面积仅占总面积的52.6%,但分布1682个地质灾害点,占灾害点总数的95.9%;北川县地质灾害危险性评价图表明地质灾害高危险区一般沿断裂线或者河流呈带状分布,说明地震诱发地质灾害有其自身特点,受发震断裂、河谷地形地貌等条件的控制。
(4)建立指标量化敏感性统计模型,实现评价因子的定量化。本文在数学统计学基础之上建立的一种指标量化敏感性统计模型,指标量化敏感性统计模型是计算各指标因子属性的地质灾害点密度和总评价区的地质灾害点平均密度之间组合频率,以组合频率大小来确定其敏感性,并进行敏感度赋值。
(5)建立了强震区地质灾害危险性评价模式,其基本流程为:包括数据采集与预处理→地质灾害遥感图像信息提取→地质灾害敏感性评价体系构建→地质灾害指标量化敏感性统计模型分析→地质灾害危险性评价体系构建(引入诱发因子)→地质灾害危险性评价→地质灾害危险性评价结果分区→地质灾害危险性评价结果成功率验证。
At 2:28 p.m, on May 12, 2008, theYingxiu town of Wenchuan County in Sichuan Province occurred 8.0 magnitude earthquake, which affected half of China, with an area of 100,000 square kilometers. The earthquake triggers the collapse of a large number of landslides. According to incomplete statistics, the earthquake-triggered geological disasters accounted to over two thousand cases, with the total estimated amount of 50,000 cases or more. The four common geological disasters, that is, rockfall, landslides, debris flow and unstable slope, take up more than 97% of the total amount. Earthquake-induced landslide is one of the most important causes of secondary disasters, since whenever a strong earthquake occurs, slope will be deformed, leading to a large number of landslides, which are called "earthquake landslide". Strong earthquake will affect the long-term stability of slopes, especially in the rainy season with continuous geological disasters and strong debris, mainly due to heavy rain which also causes a large number of landslides, that is, "storm landslide". Therefore, storm is one of the factors inducing Geologic Hazards.
Current research on“earthquake landslide”is mainly focused on its identification and characteristics, while fewer attentions are paid to dynamic analysis of characteristics of landslides induced by storm after the earthquake. Moreover, even much less researches are carried out to study the number, area, characteristics of landslides between "earthquake landslide" and" storm landslide". Given no research result is reached on the dynamic comparative analysis between "earthquake landslide" and" storm landslide" in strong earthquake zones, this paper uses multi-temporal, high-resolution aerial photos, P5, SPOT5 satellite image data to carry out point-shape remote sensing interpretation of geological hazards in Beichuan county and face-shape remote sensing interpretation of geological hazards in the typical area, with geology, geomorphology, 3S technology and other related theories and corresponding research trends home and abroad as theoretic background, and a large number of geological disasters caused by " 5.12 "Wenchuan earthquake, one strong earthquake in Beichuan County as field survey. This paper establishes remote sensing identification features of strong earthquake areas, carries out dynamic analysis of typical geological disasters in Beichuan County, and focuses on comparative analysis of number, kinds and features of "earthquake landslide" and" storm landslide". At the same time, on the basis of point-shape interpretation of all geological hazard points in Beichuan County, the present paper studies geological hazard assessment methods and systems in strong earthquake areas in Beichuan County, and establishes geological earthquake disaster risk assessment model to quickly and effectively launch assessment of geological hazards in strong earthquake areas.
Main findings of the present thesis are listed as follows:
(1) A more systematic method system of applying high precision remote sensing technology in studying geological hazards is initially established. Combined with the causes and image morphology characteristics ( shape, color, shadow, texture, etc.) of geological disasters, identification mark and interpretation method of geological disaster remote sending interpretation are amended and improved. Strengths and limitations of remote sensing technology used in geological disaster study are also elaborated.
(2) Multi-temporal, high-resolution aerial photos, P5, SPOT5 satellite images are used to dynamically analyze the amount and area of monomer landslides, collapses, landslides and regional geological disaster before the 5.12earthquake, after the 5.12 earthquake, after 9.24torrential rain in typical areas in Beichuan County. The results are: earthquake-induced landslide is one of the major causes of secondary disasters. After strong earthquake breaking out, slopes are deformed, leading to large-area landslide; rain-induced landslide is another major causes of secondary disasters, and the storm-induced geological disasters are often regional, group-occurring, simultaneous, and large disaster-causing. On the basis of two images comparison, rain storm-inducing geological disaster area is in 9.24 rainstorm is only 1/4 times of earthquake directly induced geological disaster area in 5.12 earthquake. With the passage of time and the occurrence of rainfall, rainfall induced landslide area will continue to increase; storm not only induces new landslides, but promote the revival and expansion of earthquake landslide, which expands by 1/8 of its original area. By the same token, with the passage of time and the occurrence of rainfall, earthquake landslide area continues to expand.
(3) The temporal and spatial probability concepts of geological disasters are introduced. The concept of geological hazard is perfected. Spatial distribution quantitative relationship and intrinsic relationship between the distribution and evaluation factors of earthquake-induced geological hazards are focused. Geological hazard evaluation system is established combined with storm-induced geological disaster factors. Results show that: generally, the high risk zone has a good correlation with geological hazard distribution density. Evaluation shows that despite of the area of high risk zones, which only take 52.6% of the total number of disaster areas, 1682 geological disaster points are distributed in high risk zones, accounting for 95.9% of the total number of disasters point; geological hazard assessment map of Beichuan County shows that high risk zones often distributed along fault lines or rivers, indicating that the earthquake-induced geological disasters have their own characteristics, subject to such landform controls as earthquake faults, valleys, terrain conditions, etc.
(4) Quantitative indicators of the sensitivity of statistical models are established to achieve quantitative evaluation of factors. In this paper, based on mathematical statistics, a quantitative sensitivity index statistical model is established, which calculate the combination frequency between geological disaster point density of each index factors and geological disaster point average density in the whole evaluation area. Combination frequency determines the sensitivity and sensitivity value assignment.
(5) Geological hazard assessment model in strong earthquake is established. The basis process is: data acquisition and pretreatment→interpretation of geological disaster remote sensing images→establishment of geological disaster sensitivity evaluation model→analysis of quantitative indicators of the sensitivity statistical models→establishment of geological disaster risk evaluation system (introducing induced factor)→geological hazard assessment→results of geological hazard assessment zoning→validation of geological hazard assessment.
目前对地震滑坡的研究主要集中于同发型地震滑坡的识别和特征研究,然而对震后降雨诱发的暴雨滑坡活动特征的动态分析较少,开展地震滑坡和暴雨滑坡两者之间产生的滑坡数量、面积、滑坡特征研究就更少了。鉴于目前国内尚未有针对强震区地震滑坡和暴雨滑坡进行动态对比分析的研究成果,论文基于地质学、地貌学、3S技术等相关学科理论及国内外研究进展及动态的基础上,通过对“5.12”汶川大地震强震区之一的北川县大量产生的地质灾害的野外调查基础之上,利用多时相、高分辨率的航片、P5、SPOT5卫星图像资料对北川全县进行地质灾害点状遥感解译以及对北川县典型区进行地质灾害面状遥感解译,建立强震区地质灾害遥感解译的判识特征,开展强震区北川县典型区的地质灾害动态分析,重点分析对比地震滑坡和暴雨滑坡数量类型特征,同时在完成对北川全县地质灾害点状解译的基础之上,开展强震区北川县地质灾害危险性评价方法与体系的研究,并建立强震区地质灾害危险性评价模式,可以快速有效的展开强震区地质灾害的快速评价。
论文主要得到以下研究成果:
(1)初步建立了较为系统的利用高精度遥感技术研究地质灾害的方法体系,结合崩滑流地质灾害的成因及图像形态特征(形态、色调、阴影、纹理等)修正和完善了地质灾害遥感解译的判别标志和解译方法,阐明了遥感技术在地质灾害研究中的优点和局限性。
(2)利用多时相、高分辨率的航片、P5、SPOT5卫星图像,完成对北川县典型区进行了5.12地震之前、5.12地震之后、9.24暴雨之后的单体滑坡、崩塌、泥石流和区域地质灾害数量和面积规模特征对比动态分析,表明:地震是诱发滑坡次生灾害的动力成因之一,每当强烈地震发生后,斜坡产生变形破坏,导致大面积滑坡;同时暴雨也是诱发滑坡次生灾害的动力成因之一,且暴雨诱发的地质灾害往往具有区域性、群发性、同时性、暴发性和成灾大的特点,结合两期影像对比9.24暴雨诱发的地质灾害体面积是5.12地震直接诱发滑坡面积的1/4倍,随着时间的推移和降雨的发生,降雨引起的滑坡面积将持续增加;暴雨不仅诱发新的滑坡,而且促使原来地震滑坡复活,并扩大其面积,暴雨后地震滑坡面积扩大了原来面积的1/8倍,同样随着时间的推移和降雨的发生,地震滑坡面积将持续扩大。
(3)引入地质灾害的空间概率和时间概率概念,完善了地质灾害危险性概念。重点分析了地震诱发地质灾害分布与评价指标的空间分布量化关系和内在本质联系,并结合降雨诱发地质灾害因子进行了地质灾害危险性评价体系的构建。结果表明:高危险区一般与地质灾害分布密度有较好的对应关系,评价表明尽管高危险区面积仅占总面积的52.6%,但分布1682个地质灾害点,占灾害点总数的95.9%;北川县地质灾害危险性评价图表明地质灾害高危险区一般沿断裂线或者河流呈带状分布,说明地震诱发地质灾害有其自身特点,受发震断裂、河谷地形地貌等条件的控制。
(4)建立指标量化敏感性统计模型,实现评价因子的定量化。本文在数学统计学基础之上建立的一种指标量化敏感性统计模型,指标量化敏感性统计模型是计算各指标因子属性的地质灾害点密度和总评价区的地质灾害点平均密度之间组合频率,以组合频率大小来确定其敏感性,并进行敏感度赋值。
(5)建立了强震区地质灾害危险性评价模式,其基本流程为:包括数据采集与预处理→地质灾害遥感图像信息提取→地质灾害敏感性评价体系构建→地质灾害指标量化敏感性统计模型分析→地质灾害危险性评价体系构建(引入诱发因子)→地质灾害危险性评价→地质灾害危险性评价结果分区→地质灾害危险性评价结果成功率验证。
At 2:28 p.m, on May 12, 2008, theYingxiu town of Wenchuan County in Sichuan Province occurred 8.0 magnitude earthquake, which affected half of China, with an area of 100,000 square kilometers. The earthquake triggers the collapse of a large number of landslides. According to incomplete statistics, the earthquake-triggered geological disasters accounted to over two thousand cases, with the total estimated amount of 50,000 cases or more. The four common geological disasters, that is, rockfall, landslides, debris flow and unstable slope, take up more than 97% of the total amount. Earthquake-induced landslide is one of the most important causes of secondary disasters, since whenever a strong earthquake occurs, slope will be deformed, leading to a large number of landslides, which are called "earthquake landslide". Strong earthquake will affect the long-term stability of slopes, especially in the rainy season with continuous geological disasters and strong debris, mainly due to heavy rain which also causes a large number of landslides, that is, "storm landslide". Therefore, storm is one of the factors inducing Geologic Hazards.
Current research on“earthquake landslide”is mainly focused on its identification and characteristics, while fewer attentions are paid to dynamic analysis of characteristics of landslides induced by storm after the earthquake. Moreover, even much less researches are carried out to study the number, area, characteristics of landslides between "earthquake landslide" and" storm landslide". Given no research result is reached on the dynamic comparative analysis between "earthquake landslide" and" storm landslide" in strong earthquake zones, this paper uses multi-temporal, high-resolution aerial photos, P5, SPOT5 satellite image data to carry out point-shape remote sensing interpretation of geological hazards in Beichuan county and face-shape remote sensing interpretation of geological hazards in the typical area, with geology, geomorphology, 3S technology and other related theories and corresponding research trends home and abroad as theoretic background, and a large number of geological disasters caused by " 5.12 "Wenchuan earthquake, one strong earthquake in Beichuan County as field survey. This paper establishes remote sensing identification features of strong earthquake areas, carries out dynamic analysis of typical geological disasters in Beichuan County, and focuses on comparative analysis of number, kinds and features of "earthquake landslide" and" storm landslide". At the same time, on the basis of point-shape interpretation of all geological hazard points in Beichuan County, the present paper studies geological hazard assessment methods and systems in strong earthquake areas in Beichuan County, and establishes geological earthquake disaster risk assessment model to quickly and effectively launch assessment of geological hazards in strong earthquake areas.
Main findings of the present thesis are listed as follows:
(1) A more systematic method system of applying high precision remote sensing technology in studying geological hazards is initially established. Combined with the causes and image morphology characteristics ( shape, color, shadow, texture, etc.) of geological disasters, identification mark and interpretation method of geological disaster remote sending interpretation are amended and improved. Strengths and limitations of remote sensing technology used in geological disaster study are also elaborated.
(2) Multi-temporal, high-resolution aerial photos, P5, SPOT5 satellite images are used to dynamically analyze the amount and area of monomer landslides, collapses, landslides and regional geological disaster before the 5.12earthquake, after the 5.12 earthquake, after 9.24torrential rain in typical areas in Beichuan County. The results are: earthquake-induced landslide is one of the major causes of secondary disasters. After strong earthquake breaking out, slopes are deformed, leading to large-area landslide; rain-induced landslide is another major causes of secondary disasters, and the storm-induced geological disasters are often regional, group-occurring, simultaneous, and large disaster-causing. On the basis of two images comparison, rain storm-inducing geological disaster area is in 9.24 rainstorm is only 1/4 times of earthquake directly induced geological disaster area in 5.12 earthquake. With the passage of time and the occurrence of rainfall, rainfall induced landslide area will continue to increase; storm not only induces new landslides, but promote the revival and expansion of earthquake landslide, which expands by 1/8 of its original area. By the same token, with the passage of time and the occurrence of rainfall, earthquake landslide area continues to expand.
(3) The temporal and spatial probability concepts of geological disasters are introduced. The concept of geological hazard is perfected. Spatial distribution quantitative relationship and intrinsic relationship between the distribution and evaluation factors of earthquake-induced geological hazards are focused. Geological hazard evaluation system is established combined with storm-induced geological disaster factors. Results show that: generally, the high risk zone has a good correlation with geological hazard distribution density. Evaluation shows that despite of the area of high risk zones, which only take 52.6% of the total number of disaster areas, 1682 geological disaster points are distributed in high risk zones, accounting for 95.9% of the total number of disasters point; geological hazard assessment map of Beichuan County shows that high risk zones often distributed along fault lines or rivers, indicating that the earthquake-induced geological disasters have their own characteristics, subject to such landform controls as earthquake faults, valleys, terrain conditions, etc.
(4) Quantitative indicators of the sensitivity of statistical models are established to achieve quantitative evaluation of factors. In this paper, based on mathematical statistics, a quantitative sensitivity index statistical model is established, which calculate the combination frequency between geological disaster point density of each index factors and geological disaster point average density in the whole evaluation area. Combination frequency determines the sensitivity and sensitivity value assignment.
(5) Geological hazard assessment model in strong earthquake is established. The basis process is: data acquisition and pretreatment→interpretation of geological disaster remote sensing images→establishment of geological disaster sensitivity evaluation model→analysis of quantitative indicators of the sensitivity statistical models→establishment of geological disaster risk evaluation system (introducing induced factor)→geological hazard assessment→results of geological hazard assessment zoning→validation of geological hazard assessment.
引文
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