地壳形变分析的总体最小二乘配置方法

针对地壳形变问题中观测向量和趋势项系数矩阵均含有误差的情况,研究了总体最小二乘配置平差方法,在广义总体最小二乘准则下推导了具体的解算公式和迭代算法。利用模拟的地壳水平形变和2009年意大利L’Aquila实际地震数据进行分析,结果表明,总体最小二乘配置与传统的最小二乘配置方法在地壳形变分析中的效果基本一致,并给出了相关的理论依据。     

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Comparison of CloudSat cloud liquid water paths in arctic summer using ground-based microwave radiometer

Arctic clouds strongly influence the regional radiation balance, temperature, melting of sea ice, and freezing of sea water. Despite their importance, there is a lack of systematic and reliable observations of Arctic clouds. The CloudSat satellite launched in 2006 with a 94 GHz Cloud Profiling Radar (CPR) may contribute to close this gap. Here we compare one of the key parameters, the cloud liquid water path (LWP) retrieved from CloudSat observations and from microwave radiometer (MWR) data taken during the ASCOS (Arctic Summer Cloud Ocean Study) cruise of the research vessel Oden from August to September 2008. Over the 45 days of the ASCOS cruise, collocations closer than 3 h and 100 km were found in only 9 d, and collocations closer than 1 h and 30 km in only 2 d. The poor correlations in the scatter plots of the two LWP retrievals can be explained by the patchiness of the cloud cover in these two days (August 5th and September 7th), as confirmed by coincident MODIS (Moderate-resolution Imaging Spectroradiometer) images. The averages of Oden-observed LWP values are systematically higher (40–70 g m⁻²) than the corresponding CloudSat observations (0–50 g m⁻²). These are cases of generally low LWP with presumably small droplets, and may be explained by the little sensitivity of the CPR to small droplets or by the surface clutter.     

隧道施工期超前地质预报实施方法研究

地质信息采集、超前预报与数据解译是隧道超前地质预报中的3个重要环节,首先讨论了超前开挖隧道的地质编录的工作方法,包括风险靶段分级、掌子面地质素描,进而绘制平面地质图,建立三维地质模型,计算不良地质体及含水体在相邻未开挖隧道的出露位置。在此基础上,详细讨论了仪器配合的关键问题,并开发了隧道超前地质预报自动选择程序,可以帮助操作员从中选择不同岩性地区、不同构造发育特点、不同地下水文条件下需要选择的预报方法,并建议了特殊地段的预报方法选择,最后结合工程实例讨论了几种常用的预报方法的数据解译过程中应注意的问题及典型不良地质体的判据。其结果对于隧道超前地质预报工作具有一定的借鉴与指导意义     

利用最小二乘配置识别川滇地区跨断层形变异常的研究

将最小二乘配置方法的高斯型经验协方差函数模型应用于区域跨断层形变曲线的异常辅助识别。通过川滇地区跨断层形变对芦山7.0级、康定6.3级、鲁甸6.5级等地震的前兆异常判定曲线,说明最小二乘配置内插拟合可以进行区域跨断层水准形变曲线的趋势模拟和辅助查找区间异常特征,对于跨断层形变曲线异常识别有一定意义。     

组合法精化胶州市大地水准面

本文利用全球重力位模型、胶州市地面重力观测数据、胶州市GPS水准数据和数字地面模型(DTM),采用组合法应用移去-恢复技术计算剩余大地水准面,并与地球位模型计算的高程异常进行拟合,得到该地区重力似大地水准面,再和布测、计算得到的GPS/水准所构成的几何大地水准面拟合,利用多项式拟合完成系统改正,获得最终的大地水准面结果及相关的精度信息。     

Recovery of bathymetry from altimeter data

At present, there exist two methods used to recover the bathymetry from altimeter data, i. e. the deterministic method and the stochastic method. In this paper, the principles of the two methods are introduced first. Then according to the theory of least-square collocation, a modified statistical model for recovering bathymetry from altimeter data is proposed. The new model has been used for computing the ocean depth in the South China Sea from altimeter-derived gravity anomalies. Finally the predicted depths are compared with the ship-borne depth. It shows that they agree with each other very well.     

超高阶次勒让德函数递推计算中的压缩因子和Horner求和技术

摘要：超高阶次（如2000阶次）缔合勒让德函数值的递推计算,在接近两极时达到极大的数量级（超过10的数千次方）,这导致现有递推方法在计算缔合勒让德函数值及其导数值时失效。我们通过插入压缩因子技术,提出一个修改的递推算法,并结合使用Horner求和技术计算球谐级数的部分和。试验表明,该算法至少可以计算到3600完全阶次的球谐级数式,优于现有结果。     

基于最小二乘配置法的BP神经网络GPS高呈异常拟合方法研究

传统的BP神经网络在GPS高程异常拟合应用有其一定的局限性,特别是在对于外推高程异常值方面,传统的BP神经网络的不足表现得尤为明显.针对高程异常的特性,既有趋势性也有随机性,结合BP神经网络的优点,提出一种改进型的BP神经网络高程异常拟合方法,利用最小二乘配置法综合考虑高程异常的趋势性和随机性的特点,采用BP神经网络方...     

Efficient uncertainty quantification techniques in inverse problems for Richards’ equation using coarse-scale simulation models

This paper concerns efficient uncertainty quantification techniques in inverse problems for Richards’ equation which use coarse-scale simulation models. We consider the problem of determining saturated hydraulic conductivity fields conditioned to some integrated response. We use a stochastic parameterization of the saturated hydraulic conductivity and sample using Markov chain Monte Carlo methods (MCMC). The main advantage of the method presented in this paper is the use of multiscale methods within an MCMC method based on Langevin diffusion. Additionally, we discuss techniques to combine multiscale methods with stochastic solution techniques, specifically sparse grid collocation methods. We show that the proposed algorithms dramatically reduce the computational cost associated with traditional Langevin MCMC methods while providing similar sampling performance.