The role of observations in the inverse analysis of landslide propagation

Model calibration is usually based on trial-and-error procedures that, in turn, rely on expert judgment or previously acquired experiences for similar phenomena. Efficient and reliable procedures for model calibration of the propagation stage of landslides are still needed. This paper addresses this issue by proposing an inverse analysis procedure and applying it to the case history of a short run-out landslide triggered by a rising perched water table after a heavy rainfall. It focuses on the key role played by the field observations used to set up the inverse analysis, and evaluating the reliability of the numerical simulations. It also investigates the effect of different types of optimization parameters on the inverse analysis results, referring to a mixed-phase model or to a two-phase model for the propagating soil. Several sets of observations are used; all of them refer to the soil deposit thickness at the end of propagation, but differ in both location and number of the adopted values. The numerical analysis of the case history is performed through the academic “GeoFlow_SPH” model, and model calibration by inverse analysis is conducted using the “UCODE” software. The results obtained are discussed with the aim to provide practical criteria to identify the minimum amount of information required for a satisfactory model calibration.     

宝鸡黄土区滑坡遥感调查中遥感数据尺度问题探讨

遥感数据的尺度效应决定了可识别内容与识别精度,同一区域内的遥感数据尺度不同,同样的遥感处理模型或者方法将得到不同的处理结果。以宝鸡黄土区滑坡遥感调查为基础,对不同遥感数据源的滑坡体的最小可识别面积、图斑面积精度测算、最佳及最大成图比例尺、遥感地质灾害解译对比性分析、滑坡遥感解译精度评价等等与遥感尺度有密切关系的问题进行了探讨。研究结果表明,在宝鸡黄土区,调查大、中型以上滑坡的信息,可以采用SPOT-5(2.5 m)融合图像数据,比例尺为1∶25000或1∶50000;调查中型、小型滑坡及较大滑坡体内部结构定量信息,可以采用Quick Bird(0.61 m)融合图像数据,比例尺为1∶5000。     

A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River

Most of the water quality models previously developed and used in dissolved oxygen (DO) prediction are complex. Moreover, reliable data available to develop/calibrate new DO models is scarce. Therefore, there is a need to study and develop models that can handle easily measurable parameters of a particular site, even with short length. In recent decades, computational intelligence techniques, as effective approaches for predicting complicated and significant indicator of the state of aquatic ecosystems such as DO, have created a great change in predictions. In this study, three different AI methods comprising: (1) two types of artificial neural networks (ANN) namely multi linear perceptron (MLP) and radial based function (RBF); (2) an advancement of genetic programming namely linear genetic programming (LGP); and (3) a support vector machine (SVM) technique were used for DO prediction in Delaware River located at Trenton, USA. For evaluating the performance of the proposed models, root mean square error (RMSE), Nash–Sutcliffe efficiency coefficient (NS), mean absolute relative error (MARE) and, correlation coefficient statistics (R) were used to choose the best predictive model. The comparison of estimation accuracies of various intelligence models illustrated that the SVM was able to develop the most accurate model in DO estimation in comparison to other models. Also, it was found that the LGP model performs better than the both ANNs models. For example, the determination coefficient was 0.99 for the best SVM model, while it was 0.96, 0.91 and 0.81 for the best LGP, MLP and RBF models, respectively. In general, the results indicated that an SVM model could be employed satisfactorily in DO estimation.     

Biological reduction of chlorinated solvents: Batch-scale geochemical modeling

Simulation of biodegradation of chlorinated solvents in dense non-aqueous phase liquid (DNAPL) source zones requires a model that accounts for the complexity of processes involved and that is consistent with available laboratory studies. This paper describes such a comprehensive modeling framework that includes microbially mediated degradation processes, microbial population growth and decay, geochemical reactions, as well as interphase mass transfer processes such as DNAPL dissolution, gas formation and mineral precipitation/dissolution. All these processes can be in equilibrium or kinetically controlled. A batch modeling example was presented where the degradation of trichloroethene (TCE) and its byproducts and concomitant reactions (e.g., electron donor fermentation, sulfate reduction, pH buffering by calcite dissolution) were simulated. Local and global sensitivity analysis techniques were applied to delineate the dominant model parameters and processes. Sensitivity analysis indicated that accurate values for parameters related to dichloroethene (DCE) and vinyl chloride (VC) degradation (i.e., DCE and VC maximum utilization rates, yield due to DCE utilization, decay rate for DCE/VC dechlorinators) are important for prediction of the overall dechlorination time. These parameters influence the maximum growth rate of the DCE and VC dechlorinating microorganisms and, thus, the time required for a small initial population to reach a sufficient concentration to significantly affect the overall rate of dechlorination. Self-inhibition of chlorinated ethenes at high concentrations and natural buffering provided by the sediment were also shown to significantly influence the dechlorination time. Furthermore, the analysis indicated that the rates of the competing, nonchlorinated electron-accepting processes relative to the dechlorination kinetics also affect the overall dechlorination time. Results demonstrated that the model developed is a flexible research tool that is able to provide valuable insight into the fundamental processes and their complex interactions during bioremediation of chlorinated ethenes in DNAPL source zones.     

一种InSAR大气相位建模与估计方法

为了削弱大气延迟对干涉结果的影响以提高InSAR的测量能力,本文在InSAR大气相位特征分析的基础上,研究了一种新的InSAR大气相位建模与估计方法.首先采用稳健估计确定大气垂直分层部分的模型参数,然后利用基于Matern模型的Kriging插值估计大气紊流部分,最后应用估计的大气垂直分层和紊流资料改正InSAR测量结果.利用覆盖河南义马地区的ASAR数据对本文提出的方法进行了验证,结果表明去除大气影响后,InSAR重建的DEM与参考DEM的高程差异的均方误差由19.5m降至5.3m,精度提高了约72%.同时,改正后的干涉图更合理地揭示了义马矿区的沉降漏斗情况,进一步验证了本文方法的有效性.     

TRMM数据在中国降雨侵蚀力计算中的应用

长时间序列降雨过程资料的获取一直是降雨侵蚀力计算中的一个难题。尝试利用地面实测站点数据分别对TRMM(Tropical Rainfall Measuring Mission)卫星的3B43和3B42数据进行回归建模和订正,并采用订正后的3 h平均降雨强度代替30 min最大降雨强度,同时基于TRMM数据的EI180的降雨侵蚀力算法,计算出了全国南北纬50°范围(TRMM覆盖区)内2013年月、季和年降雨侵蚀力;最后分别计算了省域和区域尺度下的降雨侵蚀力对全国尺度下的结果进行对比验证。结果表明:(1) TRMM降水数据比地面站点观测的降水量略大,但与实测站点数据具有很好的线性回归关系,其季尺度决定系数R²均较高,由此也说明了TRMM数据可以很好地反映全国范围内降雨的季节性变化。(2)利用订正后的TRMM3B42数据计算出研究区内的年均降雨侵蚀力为536.02 MJ·mm·hm^-2·h^-1·a^-1,其降雨侵蚀主要集中在5~8月份。(3) 2013年全国降雨侵蚀变化趋势由东南向西北方向逐渐降低,且沿海省份较内陆省份降雨侵蚀较高。(4)通过对年降雨侵蚀力结果与实测站点降雨量以及订正的TRMM降水数据分析表明,降雨侵蚀力与降水之间存在着紧密的二次非线性关系。(5)通过尺度验证,其中省域尺度验证误差为8.34%,区域尺度误差仅为0.24%,由此说明了TRMM数据在不同尺度下均具有良好的适应性,同时也验证了方法在不同尺度下的有效性。该方法为有效解决土壤侵蚀中降雨强度计算资料缺乏的瓶颈,同时也为降雨侵蚀力的计算提供了一条有效的途径。     

天水锻压机床厂滑坡变形破坏机制及形成演化

天水锻压机床厂滑坡(1.4×106 m3)发生于1990年8月11日, 滑坡体主要由次生黄土组成, 滑床为第四系黄土和新近系泥岩, 滑坡沿黄土-泥岩接触面发生, 属黄土接触面滑坡。通过野外调查和工程钻探对锻压机床厂滑坡变形破坏机制及形成演化进行研究, 结果表明, 该滑坡变形破坏方式表现为滑移-拉裂式, 受区内二元斜坡结构控制, 是在工程切坡和降雨、灌溉等诱发因素作用下形成; 其形成演化经历了高陡边坡形成期→滑坡孕育期→滑动面贯通临界期→滑坡启动下滑堆积期→滑坡复活变形期等过程。该滑坡目前处于欠稳定状态, 遇地震或强降雨等作用, 极有可能再次复活下滑。研究成果可为该类滑坡的防治预警提供理论依据。