Prediction of ground displacements and velocities from groundwater level changes at the Vallcebre landslide (Eastern Pyrenees, Spain)

In active landslides, the prediction of acceleration of movement is a crucial issue for the design and performance of warning systems. The landslide of Vallcebre in the Eastern Pyreenes, Spain, has been monitored since 1996 and data on rainfall, groundwater levels and ground displacements are measured on a regular basis. Displacements observed in borehole wire extensometers have shown an immediate response of the landslide to rainfall episodes. This rapid response is likely due to the presence of preferential drainage ways. The occurrence of nearly constant rates of displacement in coincidence with steady groundwater levels suggests the presence of viscous forces developed during the movement. An attempt to predict both landslide displacements and velocities was performed at Vallcebre by solving the momentum equation in which a viscous term (Bingham and power law) was added. Results show that, using similar rheological parameters for the entire landslide, computed displacements reproduce quite accurately the displacements observed at three selected wire extensometers. These results indicate that prediction of displacements from groundwater level changes is feasible.     

High-T phase transition of synthetic ANaB(LiMg)CMg5Si8O22(OH)2 amphibole: an X-ray synchrotron powder diffraction and FTIR spectroscopic study

The synthetic amphibole Na_0.95(Li_0.95Mg_1.05)Mg₅Si₈O₂₂(OH)₂ was studied in situ at high-T, using IR OH-stretching spectroscopy and synchrotron X-ray powder diffraction. At room-T the sample has P2₁ /m symmetry, as shown by the FTIR spectrum. It shows in the OH region two well-defined and intense absorptions at 3,748 and 3,712 cm⁻¹, respectively, and two minor bands at 3,667 and 3,687 cm⁻¹. The main bands are assigned to the two independent O–H groups in the primitive structure. The two minor bands evidencing the presence of small amount of vacant A-site (^A□_0.05). With increasing T, these bands shift continuously and merge into a unique absorption at high temperature. A change as a function of increasing T is revealed by the evolution of the refined unit-cell parameters, whose trend shows a transition to C2/m at about 320–330°C. The spontaneous scalar strain, fitted with a tricritical 2–6 Landau potential, gives a T _c of 325(10)°C (β parameter = 0.27). Comparison with the second-order P2₁ /m ⇔ C2/m phase transition at 255°C for synthetic amphibole ^ANa_0.8^B(Na_0.8Mg_1.2)^CMg₅Si₈O₂₂(OH)₂ indicates that the substitution of Na with Li at the B-sites strongly affects the thermodynamic character and the T _c of the phase transition. The comparison of LNMSH amphiboles with cummingtonitic ones shows that the high-T thermodynamic behaviour is affected by A-site occupancy.     

Polymorphic phase transition in Superhydrous Phase B

We synthesized superhydrous phase B (shy-B) at 22 GPa and two different temperatures: 1200°C (LT) and 1400°C (HT) using a multi-anvil apparatus. The samples were investigated by transmission electron microscopy (TEM), single crystal X-ray diffraction, Raman and IR spectroscopy. The IR spectra were collected on polycrystalline thin-films and single crystals using synchrotron radiation, as well as a conventional IR source at ambient conditions and in situ at various pressures (up to 15 GPa) and temperatures (down to −180°C). Our studies show that shy-B exists in two polymorphic forms. As expected from crystal chemistry, the LT polymorph crystallizes in a lower symmetry space group (Pnn2), whereas the HT polymorph assumes a higher symmetry space group (Pnnm). TEM shows that both modifications consist of nearly perfect crystals with almost no lattice defects or inclusions of additional phases. IR spectra taken on polycrystalline thin films exhibit just one symmetric OH band and 29 lattice modes for the HT polymorph in contrast to two intense but asymmetric OH stretching bands and at least 48 lattice modes for the LT sample. The IR spectra differ not only in the number of bands, but also in the response of the bands to changes in pressure. The pressure derivatives for the IR bands are higher for the HT polymorph indicating that the high symmetry form is more compressible than the low symmetry form. Polarized, low-temperature single-crystal IR spectra indicate that in the LT-polymorph extensive ordering occurs not only at the Mg sites but also at the hydrogen sites.     

Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R² = 0.25–0.90) was slightly worse than that of the RF algorithm (R² = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R² = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R² = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.     

Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data (Nc)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N_c), N values have been corrected (N_c) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function N_c=N_c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N_c value, is to be approximated in which N_c value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.     

高分一号遥感影像地质灾害信息提取方法研究

山区地质灾害发生频繁,受灾范围大,危害严重。提取地质灾害信息、估算受灾情况,对救灾工作极为重要。传统的遥感人工解译方法速度慢、效率低;计算机自动解译速度快但解译精度受影像质量影响大,大范围地区很难建立起普适性的解译模型。本文利用洮南市西部高分一号遥感数据,结合DEM生成三维影像,建立解译标志,解译基础地质信息,分析地质灾害成因;根据研究区泥石流地质灾害的主要影像特征,提取影像的分类属性,基于面向对象的分类方法,建立信息提取模型,快速提取出地质灾害敏感区域;再进行人机交互,精确提取出地质灾害的类型和范围,通过野外验证,该方法十分可靠,为大范围地质灾害信息快速提取和灾后救援提供科学依据。     

高光谱影像概率分类向量机分类方法研究

从分析基于支持向量机和相关向量机的高光谱影像分类方法的优势和不足出发,将基于概率分类向量机的方法用于高光谱影像分类试验。在贝叶斯理论框架下,概率分类向量机为基函数权值引入截断Gauss先验概率分布,使得不同类别的基函数权值具有不同符号的先验分布,并利用EM算法进行参数推断,得到足够稀疏的概率模型,弥补了相关向量机选取错误类别的样本作为相关向量的不足,从而有效地提高了模型的分类精度和稳定性。OMIS和PHI影像分类试验表明,概率分类向量机能够很好地应用在高光谱影像分类。     

支持向量机的车载雷达点云目标识别

为了进一步研究移动测量系统的数据处理问题,该文根据点云的基本特征,归纳了由7个特征构成的点云原始特征向量,在此基础上,结合语义环境构建了由17个特征构成的点云扩展特征向量,并采用支持向量机模型对车载LiDAR点云进行行道树点云识别的一系列实验。实验中采用粒子群优化算法和遗传算法对支持向量机进行参数寻优;采用不同特征向量和不同数目样本对点云进行学习和目标识别;分析了特征向量的学习曲线和识别精度。实验结果表明,支持向量机模型能够在行道树点云识别中取得较高的精度。     

一种裸露土壤湿度反演方法

针对目前土壤湿度反演方法研究较少且缺少实时性的现状,该文提出一种土壤湿度反演方法——最小二乘支持向量机技术。以积分方程模型为正向算法,数值模拟不同雷达参数(频率、入射角及极化)下后向散射系数随土壤含水量和地表粗糙度的变化情况。经过数据敏感性分析,选取C-波段和X-波段、小入射角下的同极化后向散射系数作为支持向量回归的训练样本信息;经过适当的训练,利用支持向量回归技术对土壤含水量进行了反演研究;并考虑通过多频率、多极化、多入射角数据的组合,消除地表粗糙度的影响,提高反演精度。模拟结果表明,该方法反演土壤湿度具有较高的精度和较好的实时性;同时,与人工神经网络方法的结果比较,证明了该方法的有效性,为土壤湿度的反演研究提供了一种方法。