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91.
针对双差计算测站坐标效率低下的问题，采用GNSSer精密单点定位方式多核并行处理测站数据，加快解算速度；针对测站速度含有随机信号导致速度场建模精度降低的问题，采用最小二乘配置来估计随机信号，并引入Helmert方差分量估计来调整噪声与信号协方差矩阵间的不合理关系，建立精度更高的速度场模型。以新疆陆态网络2011~2017年连续运行基准站为例，利用GNSSer精密单点定位方式获取坐标时间序列，大幅提升了解算效率，验证了GNSSer的可靠性和高效性，证实精密单点定位可获得与双差定位基本一致的速度信息（差异在1.5 mm/a以内）；建立新疆水平速度场格网模型，结果表明，新疆水平运动速度为27.1~34.8 mm/a，整体趋势为北向偏东，自西南到东北部，运动方向由北偏东向东偏转。 相似文献
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通过化学分析、扫描电镜以及工艺矿物学自动定量分析系统(MLA)等测试方法对河南嵩县下蒿坪金矿进行了系统的工艺矿物学研究,包括原矿化学组成、矿物组成、金的赋存状态、主要载金矿物嵌布特征以及矿物解离特性等。结果表明,该金矿中主要可回收的有价金属为金,其品位为3.75×10-6。该金矿的原矿矿物主要由石英、钾长石、钠长石、黄铁矿和铁白云石组成,此外还有少量的赤铁矿、萤石、白云石以及方解石。原矿中的金主要赋存在黄铁矿中,而黄铁矿大部分以细粒、微细粒形式嵌布在石英和长石颗粒中。原矿中自然金的含量非常少,多以单独的自然金颗粒形式存在。原矿磨至P80=0.074 mm(-0.074 mm粒级含量占80%)时载金矿物黄铁矿、方铅矿、闪锌矿的单体解离度相对较高,有利于通过浮选回收。 相似文献
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TAI Chang-Kou 《海洋学报(英文版)》2011,30(4):102-106
An attempt is made to infer the global mean sea level(GMSL) from a global tide gauge network and frame the problem in terms of the limitations of the network. The network,owing to its limited number of gauges and poor geographical distribution complicated further by unknown vertical land movements,is ill suited for measuring the GMSL. Yet it remains the only available source for deciphering the sea level rise over the last 100 a. The poor sampling characteristics of the tide gauge network have necessitated the usage of statistical inference. A linear optimal estimator based on the Gauss-Markov theorem seems well suited for the job. This still leaves a great deal of freedom in choosing the estimator. GMSL is poorly correlated with tide gauge measurements because the small uniform rise and fall of sea level are masked by the far larger regional signals. On the other hand,a regional mean sea level(RMSL) is much better correlated with the corresponding regional tide gauge measurements. Since the GMSL is simply the sum of RMSLs,the problem is transformed to one of estimating the RMSLs from regional tide gauge measurements. Specifically for the annual heating and cooling cycle,we separate the global ocean into 10-latitude bands and compute for each 10-latitude band the estimator that predicts its RMSL from tide gauges within. In the future,the statistical correlations are to be computed using satellite altimetry. However,as a first attempt,we have used numerical model outputs instead to isolate the problem so as not to get distracted by altimetry or tide gauge errors. That is,model outputs for sea level at tide gauge locations of the GLOSS network are taken as tide gauge measurements,and the RMSLs are computed from the model outputs. The results show an estimation error of approximately 2 mm versus an error of 2.7 cm if we simply average the tide gauge measurements to estimate the GMSL,caused by the much larger regional seasonal cycle and mesoscale variation plaguing the individual tide gauges. The numerical model,Los Alamos POP model Run 11 lasting 3 1/4 a,is one of the best eddy-resolving models and does a good job simulating the annual heating and cooling cycle,but it has no global or regional trend. Thus it has basically succeeded in estimating the seasonal cycle of the GMSL. This is still going to be the case even if we use the altimetry data because the RMSLs are dominated by the seasonal cycle in relatively short periods. For estimating the GMSL trend,longer records and low-pass filtering to isolate the statistical relations that are of interest. Here we have managed to avoid the much larger regional seasonal cycle plaguing individual tide gauges to get a fairly accurate estimate of the much smaller seasonal cycle in the GMSL so as to enhance the prospect of an accurate estimate of GMSL trend in short periods. One should reasonably expect to be able to do the same for longer periods during which tide gauges are plagued by much larger regional interannual(e. g.,ENSO events) and decadal sea level variations. In the future,with the availability of the satellite altimeter data,we could use the same approach adopted here to estimate the seasonal variations of GMSL and RMSL accurately and remove these seasonal variations accordingly so as to get a more accurate statistical inference between the tide gauge data and the RMSLs(therefore the GMSL) at periods longer than 1 a,i. e.,the long-term trend. 相似文献
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Sampling errors of the global mean sea level derived from TOPEX/Poseidon (T/P) altimetry are explored using 31/ 4a of eddy-resolving numerical model outputs for sea level. By definition, the sampling errors would not exist if data were available everywhere at all times. Four problems with increasing and progressively added complexities are examined to understand the causes of the sampling errors. The first problem (P1) explores the error incurred because T/P with turning latitudes near 66° latitudes does not cover the entire globe. The second problem (P2) examines, in addition, the spatial sampling issue because samples are only available along T/P ground tracks. The third problem (P3) adds the additional complexity that sea level at any along track location is sampled only once every 10 d versus every 3 d for the model (i.e., the temporal sampling issue). The fourth problem (P4) incorporates the full complexity with the addition of real T/P data outages. The numerical model (Los Alamos POP model Run 11) conserves the total water volume, thus generating no global mean sea level variation. Yet when the model sea level is sampled in the four problems (with P4 using the real T/P sampling), variations occur as manifestations of the sampling errors. The results show root-mean-squares (rms) sampling errors for P1 of 0.67 (0.75) mm for 10 d (3 d) global mean sea level, 0.78 (0.86) mm for P2, 0.79 mm for P3, and 1.07 mm for P4, whereas the amplitudes of the sampling errors can be as large as 2.0 (2.7) mm for P1, 2.1 (2.7) mm for P2, 2.2 mm for P3, and 2.5 mm for P4. The results clearly show the largest source of the sampling errors to be the lack of global coverage (i.e., P1), which the model has actually underestimated due to its own less-than-global coverage (between latitudes about 77° latitudes). We have extrapolated that a truly global model would show the rms sampling error to be 1.14 (1.28) mm for P1, thus implying a substantially larger sampling error for P4. 相似文献
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传统的Grab cut算法需要人工交互操作,无法实现SAR图像的自动分割;且SAR图像的斑点噪声干扰容易降低图像的分割质量。针对以上问题,文中以Grab Cut图像分割算法为基础,首先利用FCM算法对SAR图像进行预分割,根据预分割结果标记SAR图像中的前景与背景集合,得到两组较为准确的GMM初始化参数,迭代求得能量函数的最小化,实现SAR图像前景区域与背景区域的自动分割;并结合二维熵算法滤除SAR图像中的阴影,分割出目标区域。实验表明,利用该方法可自动分割出SAR图像中的目标,且分割质量良好。 相似文献