地震重灾区九寨沟黄木山滑坡稳定性分析及其防治对策

针对地震灾区九寨沟黄木山滑坡工程,分析了滑坡的岩土体特征,滑坡原因及其稳定性,简要的介绍了该工程的滑坡防治方案。     

遥感影像分辨率分析技术在滑坡研究中的应用

采用定性与定量相结合的方法对滑坡要素进行了深入分析。将滑坡要素划分为空间要素、时间要素和岩土性质要素3个方面,并分别讨论它们与遥感影像空间分辨率、时间分辨率和波谱分辨率的关系。首先对各滑坡要素进行了定性分析,然后提出了判识滑坡空间要素的定量依据;提出了通过遥感影像计算滑动速率的公式,以此作为按照时间分辨率选择遥感数据的依据;最后提出为判识岩土性质要素应采用高波谱分辨率遥感影像的观点。     

TDR技术在石油天然气长输管道滑坡监测中的应用研究

随着我国西南山区石油天然气常输管道的大规模建设,各类边坡问题将会层出不穷,滑坡检测将成为管道运营检测的重要组成部分。文中对实际滑坡治理工程中的TDR监测成果进行分析,将其与传统的监测手段对比,总结TDR技术在滑坡监测实际应用中的优缺点。同时,提出了TDR技术与长输管道自动化监视和控制体系-SCADA系统相结合的理念,可通过SCADA系统克服TDR技术绝大部分的缺点,在石油天然气常输管道滑坡检测中有着十分广阔的应用前景。     

基于深度学习的滑坡灾害易发性分析

滑坡灾害成因机理复杂、影响因素众多,深度学习作为当前人工智能领域的热点,能够更好地模拟滑坡灾害的形成并准确预测潜在的斜坡。为了挖掘深度学习在滑坡易发性的应用潜能,本文构建了一维、二维和三维的滑坡数据表达形式,并提出3种基于卷积神经网络模型（Convolutional Neural Networks, CNN）的滑坡易发性分析处理框架：基于CNN分类器、基于CNN与逻辑回归的融合和基于CNN集成,最后以江西省铅山县为研究对象进行验证,结果表明：所有基于CNN的易发性模型都能够获得准确且可靠的滑坡易发性分析结果。其中,基于二维数据的CNN模型在所有单分类器中预测精度最高,为78.95%。此外,二维CNN特征提取能够显著提升逻辑回归的预测精度,其准确率提升7.9%。最后,异质集成策略能够大幅度提升基于CNN分类器的滑坡预测精度,其准确率提升4.35%~8.78%。     

莱芜市主要地质灾害特征易发区划分及防治措施

该文总结了莱芜市地质灾害的总体特征,同时具体分析了各种地质灾害的类型、地理分布和主要影响因素等个体特征。以截至2012底的数据为依据,采用地质灾害综合危险性指数法,对莱芜市地质灾害易发程度进行综合分区评价,划定了莱芜市地质灾害高易发区、中易发区、低易发区和不易发区,针对不同的地质灾害种类提出了相应的防治措施。     

折射法和高密度电法在古滑坡勘探中的应用

古滑坡体地球物理调查方法技术的选择对勘探效果至关重要。以吉林省通化市拟建集安-通化高速公路滑坡体为例,采用折射波法和高密度电法,利用二维反演技术进行数据处理。结合地质信息,确定了滑坡体的形态及厚度变化,厚度最大约20 m,滑坡性质为堆积层滑坡。研究结果表明折射波法和高密度电法进行古滑坡勘探,效果较好。     

内蒙古高原中东部地区137Cs背景值的确定方法(英文)

In isotope 137 Cs tracing studies, it is a premise to determine suitable 137 Cs reference inventory(CRI) plots and the CRI values. Owing to the heterogeneous spatial distribution of 137 Cs deposition in the ground and diverse, or even irregular, operations in sampling and testing procedures, CRI determination is usually faced with many difficulties and uncertainties. In addition, more difficulties occur in an investigation of a large-scale region because of time constraints and measurement cost limitations. In this study, traditional CRI acquiring methods were summarized first, and then a new complex scheme was established, involving seven core steps and coupling the model estimate and sample measurement. The above CRI determination methodology was implemented in the central-eastern Inner Mongolia Plateau. The case study results showed that the CRI in the dark chestnut soil sub-region, located in the east and south of Xing'an City, exhibited 2447 Bq·m–2; the CRI in the aeolian sandy soil sub-region, positioned in the south central Tongliao City and central Chifeng City, showed 2430 Bq·m–2; the CRI in the sandy chernozem soil sub-region, situated in the northwestern Chifeng City, presented 2384 Bq·m–2; and the CRI in the chestnut soil sub-region, in the southern Xilin Gol City, was 2368 Bq·m–2. The newly proposed CRI determination scheme was proved effective, and the determined CRI plots and CRI values were convincing. The methodology offered a framework for 137 Cs tracing studies in large-scale regions or long-distance transects.     

南海北部深水区新生代岩浆岩分布规律及其与海底地质灾害的相关性

前人对南海北部新生代陆缘盆地结构、构造样式和期次等做了大量的研究工作,但对于具体体现内动力过程的岩浆活动关注较少,尤其是北部深水区的研究鲜有涉及。本文基于二维地震剖面的解译分析,将南海北部新生代岩浆岩划分为3个集中分布区:琼东南-西沙区、神狐区和东沙区。各区域岩浆岩的产出状态有所差异,其中琼东南-西沙区岩浆岩的平面展布规律性极强,主要受右行右阶的走滑断裂控制;神狐区岩浆岩位于NW向与NE向断裂的交汇处,侵位空间受先存断裂制约;东沙区岩浆岩的产生与东沙运动期间区域伸展过程有直接关系。作为内动力的表征,岩浆作用对于海底地形地貌具有重要的改造作用,与活动断裂、海底滑坡、浅层气等海底地质灾害也具有成因上的关联。     

地质雷达在海岛调查中的应用

地质雷达探测技术正朝着探测精度更高、探测范围更广、解释更准确的方向发展,表现出前所未有的广阔发展前景,被广泛应用于工程、环境、灾害地质调查等领域,越来越受到人们的关注。随着海岛的开发与海岛基础设施的建设,海岛工程勘查与工程质量评估和地质灾害的影响与危害评估等工作越来越多。本文章结合地质雷达探测的优势,分析了海岛开发和地质调查中的研究内容,结合海岛上的滑坡监测、浅层断裂等地质灾害勘探实例,论述了地质雷达在海岛工程与地质调查中的应用。     

Deriving airborne laser scanning based computational canopy volume for forest biomass and allometry studies

A computational canopy volume (CCV) based on airborne laser scanning (ALS) data is proposed to improve predictions of forest biomass and other related attributes like stem volume and basal area. An approach to derive the CCV based on computational geometry, topological connectivity and numerical optimization was tested with sparse-density, plot-level ALS data acquired from 40 field sample plots of 500–1000 m² located in a boreal forest in Norway. The CCV had a high correspondence with the biomass attributes considered when derived from optimized filtrations, i.e. ordered sets of simplices belonging to the triangulations based on the point data. Coefficients of determination (R²) between the CCV and total above-ground biomass, canopy biomass, stem volume, and basal area were 0.88–0.89, 0.89, 0.83–0.97, and 0.88–0.92, respectively, depending on the applied filtration. The magnitude of the required filtration was found to increase according to an increasing basal area, which indicated a possibility to predict this magnitude by means of ALS-based height and density metrics. A simple prediction model provided CCVs which had R² of 0.77–0.90 with the aforementioned forest attributes. The derived CCVs always produced complementary information and were mainly able to improve the predictions of forest biomass relative to models based on the height and density metrics, yet only by 0–1.9 percentage points in terms of relative root mean squared error. Possibilities to improve the CCVs by a further analysis of topological persistence are discussed.