The rate of grain release by pore-ice sublimation in cold-aeolian environments

Pore‐ice sublimation is a prerequisite for aeolian activity in cold environments where surface sediments hold significant amounts of frozen water. Few quantitative studies have defined the rate of grain release from cemented surfaces by pore‐ice sublimation. In 1996–1997, controlled field experiments at Presqu'ile Beach, Ontario, were implemented to measure sediment release from frozen surfaces. The release rates were compared to the local wind regime, ambient temperature and humidity. In additional field experiments, the effect of sediment water content on grain release by sublimation was examined. From the experimental results, an equation which predicts grain release based on local wind speed, ground temperature, humidity, and surface water content is proposed. Predicted release rates show reasonable agreement with natural deflation measured on the beach at Presqu'ile Provincial Park.     

南极无冰区典型沉积环境石英砂表面结构特征及其在沉积环境识别中的应用

本文利用电镜扫描方法 (SEM)对比分析了南极乔治王岛无冰区海滩沉积、冰碛物以及阿德雷岛湖泊沉积等典型沉积环境中的石英砂表面结构特征 ,得出了乔治王岛各典型沉积环境中石英砂表面结构特征的颗粒频率曲线。结果表明 :不同沉积环境下的石英砂颗粒表面结构特征具有相似性 ,这主要和南极地区广泛发育的冰川作用有关 ,同时 ,它们也有各自典型的特征组合 ,这些典型特征组合的不同反映了石英砂后期的搬运历史和沉积环境的差异。本文还依据石英砂表面特征的颗粒出现频率 ,利用聚类分析方法对研究区内三级海岸阶地上的沉积物和一个未知成因的样品进行了沉积环境的判别分析 ,结果证明石英砂微形貌特征可以作为南极地区沉积环境识别的有效手段     

沃尔什变换在测井曲线分层中的应用研究

在岩性复杂的变质岩地区实施的大陆科学钻探，是通过采集井下不同深度岩石物理化学性质和物理场的各种数据，研究地壳特征及演化过程的各种科学问题。利用测井资料达到岩性剖面的重建是大陆科学钻探的重要目的之一。章利用沃尔什变换对钻孔岩性剖面进行分层，大重建的岩性剖面结果可以看出，该方法对于变质岩地区不同岩性的划分还是比较有效的。     

测井资料交会图法在火山岩岩性识别中的应用

在火山岩储层研究中，岩性识别显得越来越重要。在评述目前常用的岩性识别方法后，重点以测井资料交会图法为例，以松辽盆地徐家围子断陷升平气田深层白垩系营城组火山岩为对象，优选出密度测井、自然伽玛测井、声波测井、电阻率、钍铀等测井项目的数据进行交会，编制出测井曲线交会图版，并以此为依据识别出该区的火山岩主要岩性有：安山岩、玄武岩、流纹岩和凝灰岩等。识别结果与实际情况相吻合。     

弹性地基接缝板模量反演和地基脱空判定

用有限单元法建立了可考虑接缝和地基脱空的弹性地基板位移计算模型。并根据系统识别原理，建立了接缝地基板弹性模量的反演分析方法。最终提出了利用FWD实测板中弯沉盆数据反演面板与地基弹性模量、根据板角(板边)弯沉值判定地基脱空面积的迭代方法。     

区域土壤环境地球化学研究——异常成因判别·环境质量·污染程度评价的思路与方法

从概念内涵和应用目标出发,认为土壤环境地球化学质量评价应该分为质量现状和污染程度两个方面.在此基础上探讨了区域地球化学资料在土壤环境现状质量评价、异常成因识别、污染元素累积速率和土壤污染程度评价研究的基本思路和方法.     

Identification of soil degradation during earthquake excitations by Bayesian inference

A Bayesian inference approach is introduced to identify soil degradation behaviours at four downhole array sites. The approach of inference is based on a parametric time‐varying infinite impulse response filter model. The approach is shown to be adaptive to the changes of filter parameters and noise amplitudes. Four sites, including the Lotung (Taiwan), Chiba (Japan), Garner Valley (California), and Treasure Island (California) sites with downhole seismic arrays are analysed. Our results show two major types of soil degradation behaviour: the well‐known strain‐dependent softening, and reduction in stiffness that is not instantaneously recoverable. It is also found that both types of soil degradation are more pronounced in sandy soils than in clayey soils. The mechanism for the second type of soil degradation is not yet clear to the authors and suggested to be further studied. Copyright © 2003 John Wiley & Sons, Ltd.     

System identification of instrumented bridge systems

Several recorded motions for seven bridge systems in California during recent earthquakes were analysed using parametric and non‐parametric system identification (SI) methods. The bridges were selected considering the availability of an adequate array of accelerometers and accounting for different structural systems, materials, geometry and soil types. The results of the application of SI methods included identification of modal frequencies and damping ratios. Excellent fits of the recorded motion in the time domain were obtained using parametric methods. The multi‐input/single‐output SI method was a suitable approach considering the instrumentation layout for these bridges. Use of the constructed linear filters for prediction purposes was also demonstrated for three bridge systems. Reasonable prediction results were obtained considering the various limitations of the procedure. Finally, the study was concluded by identifying the change of the modal frequencies and damping of a particular bridge system in time using recursive filters. Copyright © 2003 John Wiley & Sons, Ltd.     

System identification of linear structures based on Hilbert–Huang spectral analysis. Part 2: Complex modes

A method, based on the Hilbert–Huang spectral analysis, has been proposed by the authors to identify linear structures in which normal modes exist (i.e., real eigenvalues and eigenvectors). Frequently, all the eigenvalues and eigenvectors of linear structures are complex. In this paper, the method is extended further to identify general linear structures with complex modes using the free vibration response data polluted by noise. Measured response signals are first decomposed into modal responses using the method of Empirical Mode Decomposition with intermittency criteria. Each modal response contains the contribution of a complex conjugate pair of modes with a unique frequency and a damping ratio. Then, each modal response is decomposed in the frequency–time domain to yield instantaneous phase angle and amplitude using the Hilbert transform. Based on a single measurement of the impulse response time history at one appropriate location, the complex eigenvalues of the linear structure can be identified using a simple analysis procedure. When the response time histories are measured at all locations, the proposed methodology is capable of identifying the complex mode shapes as well as the mass, damping and stiffness matrices of the structure. The effectiveness and accuracy of the method presented are illustrated through numerical simulations. It is demonstrated that dynamic characteristics of linear structures with complex modes can be identified effectively using the proposed method. Copyright © 2003 John Wiley & Sons, Ltd.     

A neural network approach for structural identification and diagnosis of a building from seismic response data

This work presents a novel procedure for identifying the dynamic characteristics of a building and diagnosing whether the building has been damaged by earthquakes, using a back‐propagation neural network approach. The dynamic characteristics are directly evaluated from the weighting matrices of the neural network trained by observed acceleration responses and input base excitations. Whether the building is damaged under a large earthquake is assessed by comparing the modal parameters and responses for this large earthquake with those for a small earthquake that has not caused this building any damage. The feasibility of the approach is demonstrated through processing the dynamic responses of a five‐storey steel frame, subjected to different strengths of the Kobe earthquake, in shaking table tests. Copyright © 2002 John Wiley & Sons, Ltd.