基于弱光栅技术的地面沉降自动化监测系统

光纤监测技术具有分布式、精度高等特点,在地面沉降监测中具有独特优势。但受限于监测成本较高与监测环境复杂,目前地面沉降光纤监测多通过人工采集数据,限制了在特殊环境变化情况下地面沉降的实时信息获取。文章在地面沉降钻孔全断面光纤监测技术的基础上,设计并建立了基于弱光栅技术的地面沉降自动化监测系统。该监测系统利用弱反射光栅、时分复用、物联网和数据库等技术,通过4G无线通信手段实现了地面沉降在线自动化监测和远程数据实时采集,并通过客户端系统软件实现数据的存储、查询和分析。将其应用到衡水地区地面沉降监测中,结果表明:钻孔内土层压缩变形主要发生在以黏性土为主的隔水层（Ad2、Ad3、Ad4）;受季节性地下水开采的影响,钻孔100～400 m深度范围内砂土含水层存在波动变化,在监测期内,冬季略回弹,随后春季地下水开采量增大,地下水位下降,土层呈现压缩趋势。监测结果验证了该系统的可行性与准确性,使得整个地面沉降监测流程趋于自动化、规范化和低成本化,具有广泛的应用前景。     

人为因素导致的地面沉降

天然气、石油、地下水、地热热水和卤水等流体运移出地下储存地层时,地层耗尽了流体而产生压缩变形,这些变形传递到地表表面就形成了人为的地面沉降。在本文中:(1)列举了世界上主要的地面沉降区域;(2)论述了引发地面沉降的力学机制,这些可以量测的地面沉降发生在地下含水层体系、天然气气田和石油油田之上;(3)描述了目前可以用于地面沉降测量和岩石变形原位测试的技术手段;(4)简要介绍了几个地面沉降预测的数值模型;(5)说明了用于控制地面沉降发展和减轻相关环境影响的几种防治措施。     

关中地区垂直形变场及其动态演化特征

应用关中地区20余年的精密水准资料,用分段线性速率动态平差法进行统一起算基准的拟稳平差计算．得出了197l～1976、1976-1980、1980-1986和1986～1996年度的垂直形变速率图,从动态的观点研究了关中地区地壳垂直形变场的演化特征。研究结果表明：（1）地壳垂直形变场有规律的变化与区域应力-应变场的微动态活动有关,青藏亚板块和华北亚板块对关中地区的构造变形交替起主导作用,并产生了不同形变特征;（2）地壳垂直运动不是线性的。有加速、反向和停顿,长期平均垂直运动速率低于各时段的变化;（3）地壳垂直变化与断裂活动密切相关。垂直形变的逆继承性构造活动时期远小于继承性构造活动时期;（4）西安是一个特殊沉降区。由于过量开采地下水引起地面急剧下降。     

深圳市龙岗区同乐村岩溶地面塌陷特征及形成机理初步探讨

2004年11月9日凌晨2时许,深圳市龙岗区同乐居委会水流田村发生岩溶地面塌陷,倒房1栋,所幸无人伤亡。本文在工程地质勘察的基础上,对此次塌陷的特征及形成机理作了初步探讨,提出深埋岩溶地面塌陷,除有溶洞、地下水等必要条件,还与第四系沉积物的成分、结构、透水性密切相关,同时地质构造也起着控制作用。深埋岩溶地面塌陷与浅层地下水的多寡及垂直活动特性密切相关。     

盘西线新海站至大塘站岩溶路基塌陷发育规律及注浆整治

通过对盘西线新海站—大塘站(K 3 500~K 38 493)岩溶路基塌陷病害注浆整治的实例,介绍了该段岩溶路基塌陷发育规律、病害情况、病害的形成机理、注浆整治方法、质量检查方法等内容。     

Integration of hyperspectral and LiDAR data for mapping small water bodies

Inland water bodies are globally threatened by environmental degradation and climate change. On the other hand, new water bodies can be designed during landscape restoration (e.g. after coal mining). Effective management of new water resources requires continuous monitoring; in situ surveys are, however, extremely time-demanding. Remote sensing has been widely used for identifying water bodies. However, the use of optical imagery is constrained by accuracy problems related to the difficulty in distinguishing water features from other surfaces with low albedo, such as tree shadows. This is especially true when mapping water bodies of different sizes. To address these problems, we evaluated the potential of integrating hyperspectral data with LiDAR (hereinafter “integrative approach”). The study area consisted of several spoil heaps containing heterogeneous water bodies with a high variability of shape and size. We utilized object-based classification (Support Vector Machine) based on: (i) hyperspectral data; (ii) LiDAR variables; (iii) integration of both datasets. Besides, we classified hyperspectral data using pixel-based approaches (K-mean, spectral angle mapper). Individual approaches (hyperspectral data, LiDAR data and integrative approach) resulted in 2–22.4 % underestimation of the water surface area (i.e, omission error) and 0.4–1.5 % overestimation (i.e., commission error).The integrative approach yielded an improved discrimination of open water surface compared to other approaches (omission error of 2 % and commission error of 0.4 %). We also evaluated the success of detecting individual ponds; the integrative approach was the only one capable of detecting the water bodies with both omission and commission errors below 10 %. Finally, the assessment of misclassification reasons showed a successful elimination of shadows in the integrative approach. Our findings demonstrate that the integration of hyperspectral and LiDAR data can greatly improve the identification of small water bodies and can be applied in practice to support mapping of restoration process.     

The 2019 Brumadinho tailings dam collapse: Possible cause and impacts of the worst human and environmental disaster in Brazil

On 25th January 2019, the tailings dam of the Brumadinho iron mine operated by Vale S/A failed catastrophically. The death toll stood at 259 and 11 people remained missing as of January 2020. This tragedy occurred three years after Mariana’s tailings dam rupture – the most significant tailing dam disaster in Brazilian history. Thus far, a systematic investigation on the cause and effect of the failure has yet to be conducted. Here, we use satellite-driven soil moisture index, multispectral high-resolution imagery and Interferometric Synthetic Aperture Radar (InSAR) products to assess pre-disaster scenarios and the direct causes of the tailings dam collapse. A decreasing trend in the moisture content at the surface and the full evanescence of pond water through time (2011–2019) suggest that the water was gradually penetrating the fill downwards and caused the seepage erosion, saturating the tailings dam. Large-scale slumping of the dam (extensional failure) upon the rupture indicates that the materials of the fill were already saturated. InSAR measurements reveal a dramatic, up to 30 cm subsidence in the dam (at the rear part) within the past 12 months before the dam collapse, signifying that the sediments had been removed from the fill. Although the information on the resistance level of the tailings dam to infiltrations is not available, these pieces of evidence collectively indicate that the seepage erosion (piping) is the primary cause for the chronic weakening of the structure and, hence, the internal “liquefaction” condition. Upon the collapse, the fully saturated mud tailings flowed down the gentle slope area (3.13 × 10⁶ m²), where 73 % were originally covered by tree, grass or agricultural tracts. The toxic mud eventually reached the Paraopeba River after travelling 10 km, abruptly increasing the suspended particulate matter (SPM) concentration and the toxic chemical elements in the river, immediately affecting the local livelihoods that depend on its water. The Paraopeba River is a major tributary of the San Francisco River, the second-longest river in Brazil reaching the Atlantic Ocean. We anticipate that the environmental repercussions of this toxic seepage will be felt throughout the entire basin, especially riverine communities located downstream.     

最优权组合预测法在采煤沉陷变形预测中的应用

煤矿地表沉降变形预测多基于煤矿开采沉陷预计理论展开,基于变形分析理论的变形预测模型目前多集中在单模型预测。本文基于组合预测思想,以非等间隔灰色预测模型与BP神经网络模型为预测单模型,以陕西北部某煤矿采煤工作面上方实测地表沉降值为数据源,以最优加权法对单模型预测结果开展了最优权组合,组合模型中两种单模型的权重分别为0.466 7、0.533 3。选取部分监测点的预测结果进行模型精度评价,结果表明：3种预测模型精度均达到了一级。经对比3种模型预测结果,最优权组合预测的模型精度较单模型明显提升,预测结果较非等间隔灰色预测模型与BP神经网络预测模型有明显增益。     

利用CEEMD分析中国沿海GNSS站高程时序变化

利用完备经验模态分解方法（CEEMD）对我国沿海地区6个GNSS基准站（2010—2018）的高程时序数据进行了处理分析。结果表明：CEEMD在高程时间序列分析中具有一定的优越性,可准确分解出各GNSS站高程时序中存在的周、月、季节、年等变化周期项,其中周年运动是主要贡献项,各站高程时间序列的短周期变化与潮汐变化周期具有密切关联性;沿海GNSS站的地面沉降既具有区域的一致性,又存在区域间差异性,其中D区DBJO、DZJJ站呈现先下降后上升的趋势,N区NZUH、NWZU站呈下降趋势,B区的BZMW呈上升趋势,而同海区的BLHT站则呈显著的下降趋势。     

利用水准数据的天津市地面沉降时空特征分析

地面不均匀沉降可能对城市的发展与人民的安全造成危害,天津市的地面沉降情况尤为严重。针对该问题,本文收集天津市2005—2012年、2016—2017年水准观测数据,以固定水准点位的沉降量、沉降速率、沉降加速率为状态向量,构建卡尔曼滤波模型,对天津市历年的水准观测数据进行滤波;根据滤波后的结果,本文利用多项式加权内插的方法,以距离、沉降速率、沉降加速率信息确定权值大小,对地面沉降情况进行内插;并以中误差作为精度评定参数,比较多种内插方法的精度。通过对内插结果的试验分析发现,2005—2017年天津市地面累计平均沉降量为394.477 5 mm,最大沉降量为1 143.5 mm;主要沉降区域为北辰、大港、塘沽等地区,且随着时间的增长,这些区域呈现漏斗式下沉。试验证明本文结合卡尔曼滤波与多项式加权内插的方式能够较好地反映地面沉降的时空特征分布情况并对未来一段时间的沉降情况进行预测,对天津市的城市发展及建设有一定的参考意义。