恩施地区志留系地层斜坡灾变智能化预测研究
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摘要
滑坡是我国山区常见的一种自然地质灾害,其所造成的危害正随着经济建设的不断发展而日趋严重。当今我国实施话部大开发战略,开发的重点是基础设施建设(如水利工程、公路铁路工程、移民安置工程等)及生态环境保护;2008年,面对世界金融危机,国家决定投资4万亿元重整国内经济,其中大部分的资金也将投入到基础建设中去。届时,人类的工程建设必将为社会带来巨大的经济利益,但同时如果不注重客观自然环境的保护,以及建设中各类灾害的主动防护,则必将带来环境的进一步恶化以及不必要的人员伤亡和经济损失。
而在各种类型的地质灾害中,又以滑坡灾害的危险性及其所造成的破坏最大。然而由于滑坡灾害本身的复杂性,在很多情况下对于滑坡的准确时间预报是十分困难的,甚至是不可能的;同时对于大规模的滑坡治理而言从某种意义上讲也并不现实。因此对于滑坡灾害预测防治研究的趋势将是对特定区域、特定条件下不稳定斜坡体演变成为滑坡的可能性,以及这些不稳定斜坡体在空间分布规律上的预测研究。从而为相关部门制定建设用地规划方案提供参考依据,以减少不必要的生命财产损失。
然而,目前许多斜坡灾变空间预测模型实际上是属于“后验型”的,即通过模型的计算预测得出研究区内即有滑坡灾害的分布情况。但通常情况下,对于已经发生过的滑坡体,一方面斜坡内蓄积已久的应力通过坡体的滑动变形得以释放,另一方面滑动后的坡体其整体势能也得到了一定程度的降低,因此真正新近发生过的滑坡体将会处在一个暂时的稳定间歇期内。而真正存在危险性并对生命财产构成威胁的是如今尚未滑动,但具备滑动的一切客观条件,甚至已经出现了变形迹象,在将来外界营力的持续作用下极有可能发展演变成为滑坡的潜在不稳定天然斜坡体。因此,如何有针对性的为这一类潜在不稳定斜坡体选取合理的评价指标体系以及计算预测模型,从而能够更好的为预测其空间分布规律服务,而不是简单的预测即有滑坡体的分布情况,则是本论文研究的主要思路及创新点之一。
本次论文主要结合中国地质调查局项目“鄂西恩施地区滑坡形成机制与危险性评价”,选择鄂西恩施市地区部分志留系地层作为研究区域,通过综合系统的工程地质、环境地质调查与监测,查明鄂西恩施地区志留系地层滑坡形成的区域地学背景和关键影响因素。在对该地层上即有典型滑坡的破坏形成机理进行深入探讨的基础上,确定志留系地层上斜坡变形破坏的评价因子指标体系;进而应用GIS技术,并结合人工神经网络智能化预测模型,建立起区域潜在不稳定斜坡的空间预测模型。力求探索出一条适合鄂西山区志留系地层特点的,应用GIS技术进行斜坡灾变智能化空间预测的技术路线和方法体系。通过研究,论文主要取得了以下几个阶段性的成果:
(1)在鄂西恩施市地区,据统计,发生在志留系地层上的崩滑灾害数量占到了总数量的24.6%,即境内所有地层上大约有四分之一的灾害发生在了志留系地层上,并且在这些灾害中,中小规模的土质滑坡又占到了80%以上,斜坡的破坏类型及方式非常一致,十分有利于智能化系统对典型滑坡样本的学习记忆。所以根据典型性、代表性和资料获取程度等原则,选定鄂西恩施市屯堡乡一带的志留系地层区域作为典型研究区。采用资料收集、野外勘察、遥感资料解译等手段获取相关的基础数据,并完成专题图件的编绘及数字化工作,建立空间数据库,从而为下一步的空间预测分析提供可靠的基础资料。
(2)进行斜坡灾变智能化空间预测的前提是必须具备和待预测研究区灾害类型相似度极高的典型学习样本。对于本论文而言,典型滑坡的选择有一个非常重要的前提,即必须同样是志留系地层上所发生过的滑坡,另外与待预测研究区的空间距离最好不要过远,以保证地质环境条件的最大相似性。因此,论文选择了保扎、瞿家湾两处分别位于恩施市内及其临近周边的大型滑坡作为研究对象,来开展志留系地层滑坡形成机制的探讨。在充分掌握区域地质环境以及滑坡基本地质特征的基础上,对每个典型滑坡的滑床(母岩)、滑带、滑体等的物质组成及其特殊的工程地质性质展开定性和定量的研究。
在此基础上,论文统计总结出了志留系地层上斜坡变形破坏的特征,包括物质成分、破坏规模、滑体厚度、滑坡所在斜坡坡度以及滑坡的发生时间分布特征等等,从而对志留系地层上斜坡破坏的特殊规律性做出了梳理和总结。而后更加深入地从岩土体性质、地形地貌、坡体结构、降雨、地质构造以及人类工程活动等6个方面详细地研究了每个影响因素与斜坡体稳定性之间的关系,包括这些单因素各自影响斜坡稳定性的作用机理,以及在多因素共同作用下每个因素所具备的相对“影响力”大小。从而得出了志留系地层上斜坡发生变形破坏进而演变成为滑坡的过程机理,以及决定志留系地层上斜坡稳定性的主要影响因素及其作用方式。
(3)建立有针对性的评价指标体系。斜坡灾变空间预测的复杂性和预测的高难度性就在于它是一个由多因素确定的复杂体系,并且,这些因素在时间以及空间上还具有很强的不确定性。如何从这些因素中针对志留系地层斜坡破坏的特殊规律性,选取出主要的和控制性的稳定性影响因素,舍弃相关性较小甚至是实际上并不相关的因素,则是论文建立评价指标体系的关键点所在。
在选取影响因子的过程中,一方面既需要从志留系地层滑坡发生机理的角度来考虑影响因子问题,即因子与斜坡破坏是否有关;另一方面,也需要从利用影响因子进行斜坡灾变空间预测的角度来考虑,即各个影响因子对于灾害的产生是否具备足够的“影响力”权重,而不仅仅是是否相关,以及该影响因子是否具备大规模区域空间采集和进行图层数字化的可行性。基于这两点考虑,论文对于初步选定的影响因子逐一进行了更进一步的分析筛选,最终选定了坡度、坡体结构、降雨、人类工程活动4个指标作为志留系地层上斜坡灾变空间预测评价的指标体系构成单元。
特别对于其中的降雨指标而言,其为一个时效性变量,但论文所要进行的是灾变的空间分布预测,即评价指标应具备客观的空间分布特征,而不应随时间的变化而改变。针对这一问题,论文引入了“汇水面积”的概念:即降雨发生后,斜坡地表由于地形差异,所表现出来的汇集由雨水转化而来的地表水能力的大小。把具有时效性的降雨因子通过地表“汇水面积”的表达,转化成为了斜坡坡面汇集地表水的能力来考虑,从而也间接地将降雨作为空间静态评价指标与斜坡的稳定性联系了起来。并且通过实例计算比较,地表“汇水面积”分布不仅能够准确描述即有地面水系的分布情况;同时还能够很好的表达降雨后地表径流的分布情况,以及对斜坡稳定性同样具有至关重要作用的一些地表非常年性流水地段冲沟地貌的分布情况。
在完成指标体系影响因子的选取后,再通过不同的方案对所选取指标体系中的连续变量、线性变量以及离散变量进行量化处理。一方面防止某些变量因量纲的不同而被“夸大数值”,另一方面也为下一步智能化预测系统提供标准化的输入参数。
在本章节的最后,论文还对针对影响因子的权重赋值进行了讨论。指出了国内外针对预测指标权重分析的两类主要思路:一是以即有滑坡灾害分布图和各因素图的叠加,定量、半定量化确定各个滑坡指标的敏感性,进而确定权重;另一个是以滑坡灾害影响因素与滑坡灾害关系的理论分析,采用专家打分或评级的方法赋予各因素以权重系数。但鉴于论文所采用的智能化预测系统,是在神经网络学习记忆样本规律的基础上,模拟人脑思维的一个学习预测过程。因此指出预测模型将会在计算中,通过样本的不断学习自行动态的分配权重系数,从而也可以有效的避免人为分配权重的主观性。
(4)智能化预测理论方法的研究。针对鄂西地质环境的复杂性以及地质灾害发生的非线性,结合专业领域的最新进展,在评价预测的理论与方法上作了更加深入的探讨,将非线性理论的神经网络智能化方法引入了论文,从而为区域潜在不稳定斜坡的空间预测GIS系统提供了新的空间分析方法。
在神经网络训练样本的选取上,基于论文的主导思想,样本包含了研究区域内即有的稳定斜坡与欠稳定斜坡,以期神经网络能够对两类斜坡稳定性状态进行区别学习记忆。而样本斜坡稳定性状态的划分,则是根据野外调查评估逐一获得,区分的主要标准是斜坡上是否已具有破坏前期的明显变形现象:如坡表、坡体四周及坡上民居内是否出现张拉、剪切裂缝;裂缝是否仍在进一步变形发展中;根据坡表植被或地表水的分布情况判断坡体内地下水含量是否丰富等等。而被定义为欠稳定的斜坡,则是指目前尚未完全破坏,但在空间上具备进一步变形破坏的客观条件,在将来随着时间的推移或者外界营力的持续作用,可能会演变为滑坡的斜坡体,或者是在现有滑坡体上可能会发生二次滑动的局部坡段。从而使智能化预测系统能够围绕论文研究的主题思想有针对性的学习样本。
在对神经网络的结构、学习公式、学习方法及步骤等进行深入研究探讨的基础上,对论文所需要构建的斜坡灾变空间预测神经网络模型进行了设计。对网络输入层的层数、隐含层的层数、隐含层神经元个数、输出层的层数、权值和阀值的初始值等网络参数的选取做了详细的探讨,并且对训练函数的选取以及隐含层神经元个数的确定还专门进行了实例对比研究,最终设计出了适合于斜坡灾变空间预测问题的神经网络智能化预测模型。
(5)斜坡灾变智能化空间预测系统的集成。首先根据现场调查资料,针对研究区的具体情况,编绘以评价指标体系为主题的斜坡破坏因子专题图件,然后进行GIS图层信息采集(数值化),并建立在GIS系统管理下的各图层空间属性数据库。并且不同的斜坡破坏因子作为单独的数据层进行管理,通过分析因子与斜坡稳定性之间的关系,将每个因子图层数据按照一定规则进行分类,从而建立起研究区GIS信息图层和空间属性数据库;然后在此基础上,基于GIS的数字高程模型分析、数字地形分析、缓冲分析、水文分析、空间叠加分析等功能完成基础数据的空间处理加工分析;最后结合设计好的神经网络预测模块,集成研究区内不稳定斜坡区域的空间分布预测系统。从而形成一套完整的基于GIS的斜坡灾变智能化空间预测技术路线和方法体系。
(6)将所建立的斜坡灾变智能化空间预测系统在恩施市屯堡乡一带的志留系地层范围内进行实例预测应用。计算得出研究区内不同稳定性状态斜坡的分布图,主要是预测出潜在不稳定斜坡区的分布情况,即现在尚未破坏,但随着坡体变形的进一步加剧或是外界营力的持续作用,将来极有可能演变成为滑坡的地区。
最后,利用研究区内不同稳定性状态斜坡遥感解译的结果,对比智能化空间预测系统的成果图,对智能化空间预测系统的准确度做出定性及定量的评定。
Landslide is a common natural geological disaster in the mountainous area in our country, which is causing increasingly serious harm due to the constant development of economy. Our country today, implementing the western development strategy and especially focusing on the development of basic construction (such as water conservancy, road and railway, resettlement projects, etc.) and ecological and environmental protection; in 2008, in the face of world financial crisis, our country decided to invest 4 trillion Yuan to restructure the domestic economy, most of which will be invested in basic construction. By then, the engineering construction is going to bring enormous economic benefit to our human society, but at the same time if we do not pay attention to the protection of natural condition, as well as the positive prevention for various disasters, it will certainly bring further deterioration of the environment as well as unnecessary economic loss.
For various types of geological disaster, landslide is one of the most dangerous and damage causing type. However, because of the complexity of landslide itself, in many cases, it is very difficult sometimes even impossible to predict the exact happening time for a landslide; meanwhile it is also unfeasible for a large-scale landslide mitigation. Therefore, the research trend for landslide prevention and mitigation will be the prediction of possibility for unstable slopes which would become landslides in specific region and condition, as well as the prediction of spatial distribution law for these unstable slopes, so as to help the government for land-use planning and reducing the unnecessary loss of life and property.
However, many spatial prediction models for slope disaster are in fact belong to "type of post-confirmation", that is, predicting the distribution of landslides which have already occurred in the research area. But in normal situation, because of the slope deformation after sliding, the accumulation of the slope's internal stress has been released and its position has also been lowered, so the slope will be in a temporary stable state. But the real threat to life and property is the potential unstable slope which has all the objective conditions for sliding but still has not slid, and would become landslide in the future under continued external force. Therefore, for this kind of potential unstable slopes, how to select a reasonable evaluation index system and a prediction model, which can help better predicting the distribution condition, rather than a simple distribution prediction for post-landslides, is the main ideas and innovation of the thesis.
The thesis is based on the Project named "Formation Mechanism and Risk Assessment of landslide in Enshi region in western Hubei" from China Geological Survey. The thesis selected some Silurian strata regions in Enshi City as the study area, by systemic survey and monitoring to collect the engineering geological and environmental geological condition, so as to identify geologic background of the region and key influencing factors for the landslide which have already occurred on Silurian stratum in Enshi area. After figuring out the evaluation factor index system by deep research about the mechanism of typical landslides which occurred on Silurian stratum; then based on application of GIS technology, combined with intelligent artificial neural network prediction model, the thesis set up a spatial prediction model for potentially unstable slopes from the region. So as to approach a technical route and a systemic method which based on GIS and aiming to evaluate slope disaster on Silurian stratum in mountainous area of western Hubei. According to the research, the following results have been acquired:
(1) In Enshi City area, according to the statistics, 24.6 percent quantity of disaster occurred on Silurian stratum, that is, a quarter of the disaster has happened on Silurian Stratum. And more than 80% of these disasters are small and medium-scale soil landslides; the type of the failure is very consistent, so it is very much in favor of intelligent system to learn and remember failure samples. So according to the principle of typicality, representativeness and the extent of data acquisition, Silurian stratum in Tunbao county has been selected as a typical study area. Data collections field investigation, remote sensing data interpretation have been carried out for collecting relevant data, meanwhile digitization work for thematic maps and establishment of spatial database have also been carried out, so as to provide a reliable basic information for the coming spatial prediction analysis.
(2) The precondition for intelligent prediction of slope disaster is high similarity between disaster samples and disasters which are supposed to be predicted. For this thesis, the selection of typical landslides has a very important precondition that the chosen landslides should also occurred in Silurian stratum and not far away from the prediction area, so as to ensure the similarity of the geological condition. Therefore, Baozha landslide and Qujiawan landslide which occurred in Silurian stratum in and around Enshi City region have been selected for research about formation mechanism of landslide. Based on the knowledge of regional geological environment, as well as the basic geological feature of landslide, a qualitative and quantitative research has been carried out for the material composition, as well as special geological property of bed rock, sliding zone and sliding body of each landslide.
Based on that research, the thesis summed up the deformation and failure characteristic of Silurian stratum slope, including material composition, scale of damage, slide thickness, slope gradient for landslide and happening time of landslide, so as to summarize the special law of slope failure which occurred on Silurian stratum. Then more in-depth research on the relationship between six influencing factors like rock and soil property, topography, slope structure, rainfall, geological structure, engineering activities and slope stability has been carried out, including mechanism about the influence on slope stability by each single-factor, as well as the weight of each factor when they affect together. Consequently, the mechanism that how slopes on Silurian stratum evolve into landslides as well as the main factors that influence the slope stability has been figured out.
(3) Set up a targeted evaluation index system. The complexity and difficulty of the slope disaster prediction is that it is a complex system which consist of numerous and strongly uncertain factors. So select major and controlling factors from all those factors against the Silurian stratum slope failure and give up those less relevant or even not relevant factors, is the key point for setting up a suitable evaluation index system.
In the process of factor selection, it is required to consider that, on the one hand, the influence factor in terms of Silurian stratum landslide mechanism, that is, whether the factor is relating to slope failure; On the other hand, the influence factor in terms of using factors for spatial prediction of slope disaster, that is, whether the factor has enough "influence" to the disaster, not just whether or not related, as well as whether it is possible to collect large-scale regional spatial data and digitize all the layers. Based on these two considerations, the thesis further analyzed the selected seven factors one by one and finally selected slope, slope structure, rainfall, engineering activities as the elements of evaluation index system for spatial prediction of Silurian stratum landslide.
The rainfall factor is a temporal variable, but the prediction of the thesis is for spatial distribution of disaster, that is to say, indicators should have feature of spatial distribution, and will not changed by time. So the thesis introduced a concept namely "catchment area": that is, after the occurrence of rainfall, due to the difference of slope landform, slopes have different capacity of accumulating surface water which comes from the rainfall. Based on the concept "catchment area", the temporal rainfall variable can be translated into capacity of accumulating surface water of slopes, and thus associate rainfall as a spatial evaluation indicator with the stability of slopes indirectly. And the calculation result of "catchment area" distribution, not only be able to accurately describe the distribution of normal water system; but also be able to express a very good surface runoff distribution after rainfall, as well as distribution of gully landform where there is no perennial surface water but also plays a crucial role for the slope stability.
After all the influencing factors have been selected, then the thesis used different options to quantify these selected factors, such as continuous variable, linear variable and discrete variable. On the one hand, it can prevent "inflated values" of certain variables due to different dimension; on the other hand, it can provide standardized input parameters for the intelligent prediction system for next step.
Finally in this chapter, the thesis also discussed weight assignment of influencing factors. There are now two main ideas about analysis for weight of prediction index system: the first one is that, based on landslide hazard map and overlay map of factors, use quantitative and semi-quantitative way to determine the sensitivity of each landslide indicator, and then the weight can be determined; the other way is based on the theory analysis about relationship between influencing factors of landslide hazard and landslide hazard, then use expert marking or rating method to assign weight for each factor. However, the thesis selected an intelligent prediction system, which based on the sample law learning and memory by neural network, as well as a prediction process that simulates the thinking of a human brain. So during the calculation, the prediction model assigns the weight index automatically and dynamically by self-study of samples, which can also effectively avoid the human subjectivity that comes from the export weight assignment.
(4) Research on theory and method of intelligent prediction. Because of the complexity of geological environment in western Hubei and non-linear geological disasters, based on consideration of latest development in this field, and more in-depth discussion about the theory and method of prediction, the thesis introduced a non-linear intelligent neural network method which provides a new method for spatial unstable slope prediction GIS system.
For training sample selection, based on dominant idea of the thesis, the chosen sample contains both stable and unstable slopes in the study region; so as to let the neural network learn and remember different slope stability state separately. The division of slope stability state is confirmed by field investigation, the main division criteria based on following apparent slope deformation: such as whether the slope has tension and shear cracks on the surface, or around the slope or even in the residential area; whether the cracks are still further developing; whether the groundwater is rich in content according to the investigation of vegetation and surface water distribution, and so on. For the so called "unstable slope", it means the slope that with objective situation for further deformation and damage but has not completely destroyed yet; and in the future, with the passage of time or the outside continuous influence, which might turn into a landslide or a secondary failure on a post-landslide. So the intelligent prediction system can focus on sample study which is one of most important point of the thesis.
According to the deep research on structure, study formula, study method and steps of neural network, a neural network model has been designed for the spatial slope disaster prediction. Also according to the research on selection of input layer number, hidden layer number, hidden layer's neuron number, output layer number, weight and threshold value, as well as a special research on selection of training function and neuron number of hidden layer, ultimately the thesis designed a suitable intelligent neural network model for the spatial slope disaster prediction.
(5) Integration of intelligent spatial disaster prediction system. First of all, according to field investigation and specific condition in the research area, the thesis processed the thematic maps based on evaluation index system, as well as digitized these thematic maps, and then set up a spatial property database for these various thematic maps which all managed by GIS system. Besides, different influencing factors are managed through separate layers, according to the research on relationship between slope stability and influencing factors, the thesis classified these factor layers by certain laws, and then established the spatial property database for various thematic layers about the research region. Subsequently, based on GIS analysis of digital elevation models, digital terrain analysis, buffer analysis, hydrology analysis and spatial overlay analysis, the process for basic data has been finished. Finally, based on the designed neural network prediction model, the technology route and method system for intelligent spatial slope disaster prediction has been achieved.
(6) Apply the intelligent spatial disaster prediction model on the Silurian stratum in Tunbao County in Enshi City region. The prediction result shows the distribution of potential unstable slopes, which are slopes with objective condition for further deformation and damage but have not completely destroyed yet; and in the future, with the passage of time or the outside continuous influence, which might turn into landslides.
Subsequently, the thesis compared the result map from remote sensing interpretation with the result map from intelligent spatial prediction system, and finally made a qualitative and quantitative assessment about the accuracy of this intelligent spatial prediction system.
而在各种类型的地质灾害中,又以滑坡灾害的危险性及其所造成的破坏最大。然而由于滑坡灾害本身的复杂性,在很多情况下对于滑坡的准确时间预报是十分困难的,甚至是不可能的;同时对于大规模的滑坡治理而言从某种意义上讲也并不现实。因此对于滑坡灾害预测防治研究的趋势将是对特定区域、特定条件下不稳定斜坡体演变成为滑坡的可能性,以及这些不稳定斜坡体在空间分布规律上的预测研究。从而为相关部门制定建设用地规划方案提供参考依据,以减少不必要的生命财产损失。
然而,目前许多斜坡灾变空间预测模型实际上是属于“后验型”的,即通过模型的计算预测得出研究区内即有滑坡灾害的分布情况。但通常情况下,对于已经发生过的滑坡体,一方面斜坡内蓄积已久的应力通过坡体的滑动变形得以释放,另一方面滑动后的坡体其整体势能也得到了一定程度的降低,因此真正新近发生过的滑坡体将会处在一个暂时的稳定间歇期内。而真正存在危险性并对生命财产构成威胁的是如今尚未滑动,但具备滑动的一切客观条件,甚至已经出现了变形迹象,在将来外界营力的持续作用下极有可能发展演变成为滑坡的潜在不稳定天然斜坡体。因此,如何有针对性的为这一类潜在不稳定斜坡体选取合理的评价指标体系以及计算预测模型,从而能够更好的为预测其空间分布规律服务,而不是简单的预测即有滑坡体的分布情况,则是本论文研究的主要思路及创新点之一。
本次论文主要结合中国地质调查局项目“鄂西恩施地区滑坡形成机制与危险性评价”,选择鄂西恩施市地区部分志留系地层作为研究区域,通过综合系统的工程地质、环境地质调查与监测,查明鄂西恩施地区志留系地层滑坡形成的区域地学背景和关键影响因素。在对该地层上即有典型滑坡的破坏形成机理进行深入探讨的基础上,确定志留系地层上斜坡变形破坏的评价因子指标体系;进而应用GIS技术,并结合人工神经网络智能化预测模型,建立起区域潜在不稳定斜坡的空间预测模型。力求探索出一条适合鄂西山区志留系地层特点的,应用GIS技术进行斜坡灾变智能化空间预测的技术路线和方法体系。通过研究,论文主要取得了以下几个阶段性的成果:
(1)在鄂西恩施市地区,据统计,发生在志留系地层上的崩滑灾害数量占到了总数量的24.6%,即境内所有地层上大约有四分之一的灾害发生在了志留系地层上,并且在这些灾害中,中小规模的土质滑坡又占到了80%以上,斜坡的破坏类型及方式非常一致,十分有利于智能化系统对典型滑坡样本的学习记忆。所以根据典型性、代表性和资料获取程度等原则,选定鄂西恩施市屯堡乡一带的志留系地层区域作为典型研究区。采用资料收集、野外勘察、遥感资料解译等手段获取相关的基础数据,并完成专题图件的编绘及数字化工作,建立空间数据库,从而为下一步的空间预测分析提供可靠的基础资料。
(2)进行斜坡灾变智能化空间预测的前提是必须具备和待预测研究区灾害类型相似度极高的典型学习样本。对于本论文而言,典型滑坡的选择有一个非常重要的前提,即必须同样是志留系地层上所发生过的滑坡,另外与待预测研究区的空间距离最好不要过远,以保证地质环境条件的最大相似性。因此,论文选择了保扎、瞿家湾两处分别位于恩施市内及其临近周边的大型滑坡作为研究对象,来开展志留系地层滑坡形成机制的探讨。在充分掌握区域地质环境以及滑坡基本地质特征的基础上,对每个典型滑坡的滑床(母岩)、滑带、滑体等的物质组成及其特殊的工程地质性质展开定性和定量的研究。
在此基础上,论文统计总结出了志留系地层上斜坡变形破坏的特征,包括物质成分、破坏规模、滑体厚度、滑坡所在斜坡坡度以及滑坡的发生时间分布特征等等,从而对志留系地层上斜坡破坏的特殊规律性做出了梳理和总结。而后更加深入地从岩土体性质、地形地貌、坡体结构、降雨、地质构造以及人类工程活动等6个方面详细地研究了每个影响因素与斜坡体稳定性之间的关系,包括这些单因素各自影响斜坡稳定性的作用机理,以及在多因素共同作用下每个因素所具备的相对“影响力”大小。从而得出了志留系地层上斜坡发生变形破坏进而演变成为滑坡的过程机理,以及决定志留系地层上斜坡稳定性的主要影响因素及其作用方式。
(3)建立有针对性的评价指标体系。斜坡灾变空间预测的复杂性和预测的高难度性就在于它是一个由多因素确定的复杂体系,并且,这些因素在时间以及空间上还具有很强的不确定性。如何从这些因素中针对志留系地层斜坡破坏的特殊规律性,选取出主要的和控制性的稳定性影响因素,舍弃相关性较小甚至是实际上并不相关的因素,则是论文建立评价指标体系的关键点所在。
在选取影响因子的过程中,一方面既需要从志留系地层滑坡发生机理的角度来考虑影响因子问题,即因子与斜坡破坏是否有关;另一方面,也需要从利用影响因子进行斜坡灾变空间预测的角度来考虑,即各个影响因子对于灾害的产生是否具备足够的“影响力”权重,而不仅仅是是否相关,以及该影响因子是否具备大规模区域空间采集和进行图层数字化的可行性。基于这两点考虑,论文对于初步选定的影响因子逐一进行了更进一步的分析筛选,最终选定了坡度、坡体结构、降雨、人类工程活动4个指标作为志留系地层上斜坡灾变空间预测评价的指标体系构成单元。
特别对于其中的降雨指标而言,其为一个时效性变量,但论文所要进行的是灾变的空间分布预测,即评价指标应具备客观的空间分布特征,而不应随时间的变化而改变。针对这一问题,论文引入了“汇水面积”的概念:即降雨发生后,斜坡地表由于地形差异,所表现出来的汇集由雨水转化而来的地表水能力的大小。把具有时效性的降雨因子通过地表“汇水面积”的表达,转化成为了斜坡坡面汇集地表水的能力来考虑,从而也间接地将降雨作为空间静态评价指标与斜坡的稳定性联系了起来。并且通过实例计算比较,地表“汇水面积”分布不仅能够准确描述即有地面水系的分布情况;同时还能够很好的表达降雨后地表径流的分布情况,以及对斜坡稳定性同样具有至关重要作用的一些地表非常年性流水地段冲沟地貌的分布情况。
在完成指标体系影响因子的选取后,再通过不同的方案对所选取指标体系中的连续变量、线性变量以及离散变量进行量化处理。一方面防止某些变量因量纲的不同而被“夸大数值”,另一方面也为下一步智能化预测系统提供标准化的输入参数。
在本章节的最后,论文还对针对影响因子的权重赋值进行了讨论。指出了国内外针对预测指标权重分析的两类主要思路:一是以即有滑坡灾害分布图和各因素图的叠加,定量、半定量化确定各个滑坡指标的敏感性,进而确定权重;另一个是以滑坡灾害影响因素与滑坡灾害关系的理论分析,采用专家打分或评级的方法赋予各因素以权重系数。但鉴于论文所采用的智能化预测系统,是在神经网络学习记忆样本规律的基础上,模拟人脑思维的一个学习预测过程。因此指出预测模型将会在计算中,通过样本的不断学习自行动态的分配权重系数,从而也可以有效的避免人为分配权重的主观性。
(4)智能化预测理论方法的研究。针对鄂西地质环境的复杂性以及地质灾害发生的非线性,结合专业领域的最新进展,在评价预测的理论与方法上作了更加深入的探讨,将非线性理论的神经网络智能化方法引入了论文,从而为区域潜在不稳定斜坡的空间预测GIS系统提供了新的空间分析方法。
在神经网络训练样本的选取上,基于论文的主导思想,样本包含了研究区域内即有的稳定斜坡与欠稳定斜坡,以期神经网络能够对两类斜坡稳定性状态进行区别学习记忆。而样本斜坡稳定性状态的划分,则是根据野外调查评估逐一获得,区分的主要标准是斜坡上是否已具有破坏前期的明显变形现象:如坡表、坡体四周及坡上民居内是否出现张拉、剪切裂缝;裂缝是否仍在进一步变形发展中;根据坡表植被或地表水的分布情况判断坡体内地下水含量是否丰富等等。而被定义为欠稳定的斜坡,则是指目前尚未完全破坏,但在空间上具备进一步变形破坏的客观条件,在将来随着时间的推移或者外界营力的持续作用,可能会演变为滑坡的斜坡体,或者是在现有滑坡体上可能会发生二次滑动的局部坡段。从而使智能化预测系统能够围绕论文研究的主题思想有针对性的学习样本。
在对神经网络的结构、学习公式、学习方法及步骤等进行深入研究探讨的基础上,对论文所需要构建的斜坡灾变空间预测神经网络模型进行了设计。对网络输入层的层数、隐含层的层数、隐含层神经元个数、输出层的层数、权值和阀值的初始值等网络参数的选取做了详细的探讨,并且对训练函数的选取以及隐含层神经元个数的确定还专门进行了实例对比研究,最终设计出了适合于斜坡灾变空间预测问题的神经网络智能化预测模型。
(5)斜坡灾变智能化空间预测系统的集成。首先根据现场调查资料,针对研究区的具体情况,编绘以评价指标体系为主题的斜坡破坏因子专题图件,然后进行GIS图层信息采集(数值化),并建立在GIS系统管理下的各图层空间属性数据库。并且不同的斜坡破坏因子作为单独的数据层进行管理,通过分析因子与斜坡稳定性之间的关系,将每个因子图层数据按照一定规则进行分类,从而建立起研究区GIS信息图层和空间属性数据库;然后在此基础上,基于GIS的数字高程模型分析、数字地形分析、缓冲分析、水文分析、空间叠加分析等功能完成基础数据的空间处理加工分析;最后结合设计好的神经网络预测模块,集成研究区内不稳定斜坡区域的空间分布预测系统。从而形成一套完整的基于GIS的斜坡灾变智能化空间预测技术路线和方法体系。
(6)将所建立的斜坡灾变智能化空间预测系统在恩施市屯堡乡一带的志留系地层范围内进行实例预测应用。计算得出研究区内不同稳定性状态斜坡的分布图,主要是预测出潜在不稳定斜坡区的分布情况,即现在尚未破坏,但随着坡体变形的进一步加剧或是外界营力的持续作用,将来极有可能演变成为滑坡的地区。
最后,利用研究区内不同稳定性状态斜坡遥感解译的结果,对比智能化空间预测系统的成果图,对智能化空间预测系统的准确度做出定性及定量的评定。
Landslide is a common natural geological disaster in the mountainous area in our country, which is causing increasingly serious harm due to the constant development of economy. Our country today, implementing the western development strategy and especially focusing on the development of basic construction (such as water conservancy, road and railway, resettlement projects, etc.) and ecological and environmental protection; in 2008, in the face of world financial crisis, our country decided to invest 4 trillion Yuan to restructure the domestic economy, most of which will be invested in basic construction. By then, the engineering construction is going to bring enormous economic benefit to our human society, but at the same time if we do not pay attention to the protection of natural condition, as well as the positive prevention for various disasters, it will certainly bring further deterioration of the environment as well as unnecessary economic loss.
For various types of geological disaster, landslide is one of the most dangerous and damage causing type. However, because of the complexity of landslide itself, in many cases, it is very difficult sometimes even impossible to predict the exact happening time for a landslide; meanwhile it is also unfeasible for a large-scale landslide mitigation. Therefore, the research trend for landslide prevention and mitigation will be the prediction of possibility for unstable slopes which would become landslides in specific region and condition, as well as the prediction of spatial distribution law for these unstable slopes, so as to help the government for land-use planning and reducing the unnecessary loss of life and property.
However, many spatial prediction models for slope disaster are in fact belong to "type of post-confirmation", that is, predicting the distribution of landslides which have already occurred in the research area. But in normal situation, because of the slope deformation after sliding, the accumulation of the slope's internal stress has been released and its position has also been lowered, so the slope will be in a temporary stable state. But the real threat to life and property is the potential unstable slope which has all the objective conditions for sliding but still has not slid, and would become landslide in the future under continued external force. Therefore, for this kind of potential unstable slopes, how to select a reasonable evaluation index system and a prediction model, which can help better predicting the distribution condition, rather than a simple distribution prediction for post-landslides, is the main ideas and innovation of the thesis.
The thesis is based on the Project named "Formation Mechanism and Risk Assessment of landslide in Enshi region in western Hubei" from China Geological Survey. The thesis selected some Silurian strata regions in Enshi City as the study area, by systemic survey and monitoring to collect the engineering geological and environmental geological condition, so as to identify geologic background of the region and key influencing factors for the landslide which have already occurred on Silurian stratum in Enshi area. After figuring out the evaluation factor index system by deep research about the mechanism of typical landslides which occurred on Silurian stratum; then based on application of GIS technology, combined with intelligent artificial neural network prediction model, the thesis set up a spatial prediction model for potentially unstable slopes from the region. So as to approach a technical route and a systemic method which based on GIS and aiming to evaluate slope disaster on Silurian stratum in mountainous area of western Hubei. According to the research, the following results have been acquired:
(1) In Enshi City area, according to the statistics, 24.6 percent quantity of disaster occurred on Silurian stratum, that is, a quarter of the disaster has happened on Silurian Stratum. And more than 80% of these disasters are small and medium-scale soil landslides; the type of the failure is very consistent, so it is very much in favor of intelligent system to learn and remember failure samples. So according to the principle of typicality, representativeness and the extent of data acquisition, Silurian stratum in Tunbao county has been selected as a typical study area. Data collections field investigation, remote sensing data interpretation have been carried out for collecting relevant data, meanwhile digitization work for thematic maps and establishment of spatial database have also been carried out, so as to provide a reliable basic information for the coming spatial prediction analysis.
(2) The precondition for intelligent prediction of slope disaster is high similarity between disaster samples and disasters which are supposed to be predicted. For this thesis, the selection of typical landslides has a very important precondition that the chosen landslides should also occurred in Silurian stratum and not far away from the prediction area, so as to ensure the similarity of the geological condition. Therefore, Baozha landslide and Qujiawan landslide which occurred in Silurian stratum in and around Enshi City region have been selected for research about formation mechanism of landslide. Based on the knowledge of regional geological environment, as well as the basic geological feature of landslide, a qualitative and quantitative research has been carried out for the material composition, as well as special geological property of bed rock, sliding zone and sliding body of each landslide.
Based on that research, the thesis summed up the deformation and failure characteristic of Silurian stratum slope, including material composition, scale of damage, slide thickness, slope gradient for landslide and happening time of landslide, so as to summarize the special law of slope failure which occurred on Silurian stratum. Then more in-depth research on the relationship between six influencing factors like rock and soil property, topography, slope structure, rainfall, geological structure, engineering activities and slope stability has been carried out, including mechanism about the influence on slope stability by each single-factor, as well as the weight of each factor when they affect together. Consequently, the mechanism that how slopes on Silurian stratum evolve into landslides as well as the main factors that influence the slope stability has been figured out.
(3) Set up a targeted evaluation index system. The complexity and difficulty of the slope disaster prediction is that it is a complex system which consist of numerous and strongly uncertain factors. So select major and controlling factors from all those factors against the Silurian stratum slope failure and give up those less relevant or even not relevant factors, is the key point for setting up a suitable evaluation index system.
In the process of factor selection, it is required to consider that, on the one hand, the influence factor in terms of Silurian stratum landslide mechanism, that is, whether the factor is relating to slope failure; On the other hand, the influence factor in terms of using factors for spatial prediction of slope disaster, that is, whether the factor has enough "influence" to the disaster, not just whether or not related, as well as whether it is possible to collect large-scale regional spatial data and digitize all the layers. Based on these two considerations, the thesis further analyzed the selected seven factors one by one and finally selected slope, slope structure, rainfall, engineering activities as the elements of evaluation index system for spatial prediction of Silurian stratum landslide.
The rainfall factor is a temporal variable, but the prediction of the thesis is for spatial distribution of disaster, that is to say, indicators should have feature of spatial distribution, and will not changed by time. So the thesis introduced a concept namely "catchment area": that is, after the occurrence of rainfall, due to the difference of slope landform, slopes have different capacity of accumulating surface water which comes from the rainfall. Based on the concept "catchment area", the temporal rainfall variable can be translated into capacity of accumulating surface water of slopes, and thus associate rainfall as a spatial evaluation indicator with the stability of slopes indirectly. And the calculation result of "catchment area" distribution, not only be able to accurately describe the distribution of normal water system; but also be able to express a very good surface runoff distribution after rainfall, as well as distribution of gully landform where there is no perennial surface water but also plays a crucial role for the slope stability.
After all the influencing factors have been selected, then the thesis used different options to quantify these selected factors, such as continuous variable, linear variable and discrete variable. On the one hand, it can prevent "inflated values" of certain variables due to different dimension; on the other hand, it can provide standardized input parameters for the intelligent prediction system for next step.
Finally in this chapter, the thesis also discussed weight assignment of influencing factors. There are now two main ideas about analysis for weight of prediction index system: the first one is that, based on landslide hazard map and overlay map of factors, use quantitative and semi-quantitative way to determine the sensitivity of each landslide indicator, and then the weight can be determined; the other way is based on the theory analysis about relationship between influencing factors of landslide hazard and landslide hazard, then use expert marking or rating method to assign weight for each factor. However, the thesis selected an intelligent prediction system, which based on the sample law learning and memory by neural network, as well as a prediction process that simulates the thinking of a human brain. So during the calculation, the prediction model assigns the weight index automatically and dynamically by self-study of samples, which can also effectively avoid the human subjectivity that comes from the export weight assignment.
(4) Research on theory and method of intelligent prediction. Because of the complexity of geological environment in western Hubei and non-linear geological disasters, based on consideration of latest development in this field, and more in-depth discussion about the theory and method of prediction, the thesis introduced a non-linear intelligent neural network method which provides a new method for spatial unstable slope prediction GIS system.
For training sample selection, based on dominant idea of the thesis, the chosen sample contains both stable and unstable slopes in the study region; so as to let the neural network learn and remember different slope stability state separately. The division of slope stability state is confirmed by field investigation, the main division criteria based on following apparent slope deformation: such as whether the slope has tension and shear cracks on the surface, or around the slope or even in the residential area; whether the cracks are still further developing; whether the groundwater is rich in content according to the investigation of vegetation and surface water distribution, and so on. For the so called "unstable slope", it means the slope that with objective situation for further deformation and damage but has not completely destroyed yet; and in the future, with the passage of time or the outside continuous influence, which might turn into a landslide or a secondary failure on a post-landslide. So the intelligent prediction system can focus on sample study which is one of most important point of the thesis.
According to the deep research on structure, study formula, study method and steps of neural network, a neural network model has been designed for the spatial slope disaster prediction. Also according to the research on selection of input layer number, hidden layer number, hidden layer's neuron number, output layer number, weight and threshold value, as well as a special research on selection of training function and neuron number of hidden layer, ultimately the thesis designed a suitable intelligent neural network model for the spatial slope disaster prediction.
(5) Integration of intelligent spatial disaster prediction system. First of all, according to field investigation and specific condition in the research area, the thesis processed the thematic maps based on evaluation index system, as well as digitized these thematic maps, and then set up a spatial property database for these various thematic maps which all managed by GIS system. Besides, different influencing factors are managed through separate layers, according to the research on relationship between slope stability and influencing factors, the thesis classified these factor layers by certain laws, and then established the spatial property database for various thematic layers about the research region. Subsequently, based on GIS analysis of digital elevation models, digital terrain analysis, buffer analysis, hydrology analysis and spatial overlay analysis, the process for basic data has been finished. Finally, based on the designed neural network prediction model, the technology route and method system for intelligent spatial slope disaster prediction has been achieved.
(6) Apply the intelligent spatial disaster prediction model on the Silurian stratum in Tunbao County in Enshi City region. The prediction result shows the distribution of potential unstable slopes, which are slopes with objective condition for further deformation and damage but have not completely destroyed yet; and in the future, with the passage of time or the outside continuous influence, which might turn into landslides.
Subsequently, the thesis compared the result map from remote sensing interpretation with the result map from intelligent spatial prediction system, and finally made a qualitative and quantitative assessment about the accuracy of this intelligent spatial prediction system.
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