基于突变级数法的页岩气探采综合环境影响评价研究

为缓解能源紧张,页岩气作为储量丰富的非常规能源在我国正大力开发,而我国页岩气开发起步较晚,受管理与技术限制,页岩气开发造成的综合环境影响问题还得不到有效解决。针对页岩气开发存在的环境问题,本文基于我国页岩气开发现状,借鉴美国页岩气开发的成功经验,从自然环境与人文环境两方面对页岩气开发综合环境影响因素进行分析,建立了页岩气开发综合环境影响评价指标体系;同时为降低评价过程主观因素的影响,简化评估过程,本文引入突变级数法建立了页岩气开发综合环境影响评估模型,运用数值替换方法区分页岩气开发综合环境影响等级。最后将评价体系与评估模型应用到实际案例中,得到的评价结果与实际情况基本符合,为页岩气开发项目的综合环境评价提供了很好的理论依据。     

基于Hyperion高光谱数据和随机森林方法的岩性分类与分析

探索利用高光谱数据的岩性填图新方法是遥感地质应用领域的重要需求之一。本文运用随机森林方法和EO-1Hyperion高光谱数据,对新疆塔里木西北部柯坪地区的局部区域进行岩性分类,并对相关问题进行分析。分别利用光谱特征以及加入光谱一阶导数特征进行岩性分类,并对不同特征对岩性分类的重要性进行分析,同时与现有的基于光谱角制图方法(SAM)进行比较。结果表明,与SAM方法相比,随机森林方法得到了更高精度的岩性分类结果,是一种有效可行的岩性分类方法。根据特征重要性的排序,蓝绿光波段、短波红外波段以及相应的一阶导数特征对研究区Hyperion数据的沉积岩岩性分类贡献更大。     

地震叠前反演方法预测页岩脆性指数在南川地区的应用

页岩气的有效开发依赖水平井的压裂效果,而页岩脆性是影响页岩压裂的关键因素。通过对比分析现有的3种脆性指数计算方法,即矿物脆性指数计算法、岩石力学脆性指数计算法、基于多元回归脆性指数计算法,优选基于多元回归的脆性指数计算法计算五峰—龙马溪组(简称龙马溪组)页岩储层脆性指数。以四川盆地东部南川地区龙马溪组海相页岩气为例,利用南川地区三维地震资料叠前反演方法,获取多元回归脆性指数计算方程所需的参数,预测页岩脆性指数。结果表明,在已有钻井矿物脆性指标约束下,叠前反演法计算的脆性指数平面预测效果较好,且与实际压裂数据具有较好的一致性,能够满足南川地区页岩气勘探开发的需要。     

不同方法评价喀斯特煤矿区农田土壤重金属生态风险比较

采用TCLP法、内梅罗综合污染指数、嵌入Igeo综合污染指数法等方法对贵州织金县某煤矿区周边农田土壤重金属进行评价。土壤中Cd、Cr、Cu、Ni、Hg和Zn的含量范围分别为0.79～2.08 mg·kg-1、144.20～464.40 mg·kg-1、91.63～187.50 mg·kg-1、48.13～104.47 mg·kg-1、0.14～2.33 mg·kg-1、144.35～265.89 mg·kg-1,所有土壤样点的Cd、Cr、Cu和Ni含量均已超过国家土壤环境质量二级标准,部分土壤样点的Hg和Zn含量超过二级标准。TCLP法评价结果所有土壤样点均未受到重金属污染;内梅罗综合污染指数法评价结果为农田土壤重金属生态风险处于安全水平;嵌入Igeo综合污染指数法评价结果表明,90%的土壤达到中度污染以上,其中65%的土壤处于中度污染,25%的土壤受到中度至重度污染;潜在生态危害指数法评价表明,所有土壤样点重金属均处于轻微生态风险水平。针对不同评价方法得出的结果存在差异的情况,在评价土壤重金属污染状况时,应根据评价目标的差异选择合适的评价方法。      

Use of LiDAR-derived DEM and a stream length-gradient index approach to investigation of landslides in Zagros Mountains,Iran

This paper presents an approach to stream length-gradient index analysis to identify tectonic signatures. The graded profile of the Dez River in Zagros Mountains, Iran, indicates that the area has been tectonically disturbed, and it triggers landslide hazards. The high-gradient index shows that a steeper gradient could be potentially a signature for landslides identification. The digital surface models acquired by airborne LiDAR were used in this study to generate the HRDEM. Our result shows a great potential for improving landslide investigations by implementing stream length-gradient index derived from the HRDEM in conjunction with the landslide inventories data-set in the GIS environment. We also identified a correlation between the stream length-gradient index and the graded topographic profile with slopes and landslides. This empirical approach was verified by geodata analytics and landslide inventories data-set in conjunction with field observations. This study has identified the locations of high-gradient indices with susceptible to landslides.     

Examining human heat stress with remote sensing technology

Heat stress as an environmental hazard which can seriously affect productivity, health, or even survival of individuals has long been studied. Despite the endeavors that have been made to address the issue quantitatively with various heat stress indices, they are often measured at scatter sites. This research devotes to revealing human heat stress within continuous space with remote sensing technology. The study began with the retrieval of dry-bulb temperature from land surface temperature (LST) with empirical models. As wet-bulb temperature was calculated from dry-bulb temperature and relative humidity, discomfort index (DI) as an indicator of heat stress was revealed for the study area at 1 km spatial resolution for three summer days. Results indicated that DI can be derived within continuous space with remotely sensed data, and its spatial distribution can be dramatically affected by relative humidity. Further comparison between DI and LST indicated that LST as a widely utilized indicator of surface thermal condition fails to address human heat stress as environmental factors such as relative humidity are not taken into account.     

成层软土地基预固结处理后桩基水平承载力估算方法

目前通过对软土地基预加固处理来提高桩基水平承载力已被工程界认可,但如何在工程前期设计过程中估算软土地基预处理后桩基水平承载力提高值仍是技术难点。基于此,参考Bowles[1]的地基土水平抗力计算式,同时考虑成层软土地基预排水固结处理影响,通过数学推导,推求出根据原状软土室内土工试验抗剪强度指标及预加固处理时间,估算软土地基预处理后桩基水平承载力提高值的实用计算方法。考虑桩侧土弹塑性屈服影响,推导出成层软土中水平受荷桩弹塑性解析解及塑性区深度的计算式,给出了桩顶水平位移、桩身最大弯矩的无量纲计算式及相关计算源代码。依托于浙江省某水闸桩基工程案例,根据提出的计算方法对桩基水平承载力、桩顶水平位移及桩身最大弯矩等性状进行预估计算,并与地基预处理前、后现场试桩检测值进行验证对比,认为桩基水平承载力、桩顶水平位移及桩身最大弯矩等预估计算成果与工程现场试桩的检测值较接近,对类似工程设计具有较好的参考价值。     

基于多源遥感影像的长春市城市建成区提取

夜光遥感影像记录的城市灯光与人类活动密切相关,已广泛应用于城市信息提取。珞珈一号作为新一代夜光遥感数据源,比以往的夜光数据具有更高的空间分辨率和光谱分辨率,可以更清晰地表达城市建成区范围和内部结构。本文利用珞珈一号夜光遥感影像,通过人类居住指数（human settlement index, HSI）、植被覆盖和建筑共同校正的城市夜光指数（vegetation and build adjusted nighttime light urban index, VBANUI）及支持向量机（support vector machine, SVM）监督分类3种方法对长春市城市建成区进行提取,并与利用NPP/VIIRS（suomi national polar-orbiting partnership/visible infrared imaging radiometer suite）夜光遥感影像、采用同样方法得到的结果对比。结果显示：本文提出的VBANUI提高了传统植被覆盖校正的城市夜光指数（vegetation adjusted nighttime light urban index, VANUI）的提取精度,使用珞珈一号夜光遥感影像通过VBANUI提取的城市建成区结果最优,其Kappa系数为0.80,总体分类精度为90.74%;使用珞珈一号和NPP/VIIRS夜光遥感影像通过HSI按最佳阈值提取城市建成区的Kappa系数分别为0.75和0.72,总体分类精度分别为88.27%和86.54%;复合数据的SVM监督分类法中Landsat-NDBI、Landsat-NDBI-VIIRS、Landsat-NDBI-LJ和Landsat-NDBI-LJlog的Kappa系数分别为0.602、0.627、0.643和0.681,总体分类精度分别为81.11%、81.52%、82.25%和84.48%。研究结果表明：3种提取方法下,均为使用珞珈一号夜光遥感影像的结果优于使用NPP/VIIRS夜光遥感影像的结果,证明相比于NPP/VIIRS夜光遥感影像,珞珈一号夜光遥感影像更适用于城市尺度的建成区范围提取。     

滑坡对降雨响应的多指标监测及综合预警探析:以赣南罗坳滑坡为例

降雨量和位移是当前降雨型滑坡监测预警最常用的指标。然而,降雨量和位移监测结果只能反映降雨作用下滑坡的变形情况,不能揭示滑坡内在物理力学性状对降雨的响应。因此,除降雨量和位移监测之外,建立包括体积含水率、基质吸力等反映滑坡动态演化过程的关键指标监测体系必将成为今后更真实地把握滑坡内在演化趋势、更准确地建立滑坡综合预警判据的最有效手段。笔者对赣南地区典型降雨型滑坡进行了多指标监测及综合预警示范研究。结果表明:(1)在降雨条件下滑坡土体内部体积含水率、基质吸力和温度等多指标均产生有规律的动态响应;(2)随着降雨的持续,滑体体积含水率与基质吸力的变化均具有显著的滞后现象;(3)体积含水率和基质吸力变化速率与滑体位移具有显著的正相关性;(4)滑体温度分布变化规律受大气温度和体积含水率的共同影响。以实测数据的滑坡稳定性分析为基准,在考虑实际降雨入渗深度与滑坡稳定性的关联度上,建立了包括日降雨量、体积含水率增加速率、基质吸力减小速率以及位移速度多元指标预警方法体系,提出了基于关键指标综合预警体系及确定方法,旨在为降雨滑坡准确预警提供新模式。     

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index(BI)from basic suites of well logs is desir-able as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation(Texas).This transparent open box(TOB)algorithm matches data records by calculating the sum of squared errors be-tween their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error(RMSE)between calculated and predicted(BI).The prediction accuracy achieved by TOB using just five well logs(Gr,pb,Ns,Rs,Dt)to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE～0.056 and R2～0.790.At a sampling density of about one sample per 0.1 ft BI is predicted with RMSE～0.008 and R2～0.995.Adding a stratigraphic height index as an additional(sixth)input variable method improves BI prediction accuracy to RMSE～0.003 and R2～0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of＞±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measure-ments but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.