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由于卫星重力梯度观测的有色噪声特性和海量观测特征,在利用直接法进行重力场模型的最小二乘求解时,观测值的协方差阵为超大型的非对角阵,这给数值求解带来了极大困难.本文提出了一种基于先验误差功率谱密度的最优ARMA滤波模型构建方法,结合法方程的分块求解策略,可实现对卫星重力梯度观测值的高效滤波处理.数值仿真结果表明,利用最优ARMA滤波器进行时域滤波后,法方程的态性得到了明显改善,重力梯度观测值中的有色噪声得到了有效的"白化"处理,大地水准面精度得到了显著提升. 相似文献
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Soil surface roughness (SSR) is an important factor in controlling sediment and runoff generation, influencing directly a wide spectrum of erosion parameters. SSR is highly variable in time and space under natural conditions, and characterizing SSR to improve the parameterization of hydrological and erosion models has proved challenging. Our study uses recent technological and algorithmic developments in capturing and processing close aerial sensing data to evaluate how high-resolution imagery can assist the temporally and spatially explicit monitoring of SSR. We evaluated the evolution of SSR under natural rainfall and growing vegetation conditions on two arable fields in Denmark. Unmanned aerial vehicle (UAV) photogrammetry was used to monitor small field plots over 7 months after seeding of winter wheat following conventional and reduced tillage treatments. Field campaigns were conducted at least once a month from October until April, resulting in nine time steps of data acquisition. Structure from motion photogrammetry was used to derive high-resolution point clouds with an average ground sampling distance of 2.7 mm and a mean ground control point accuracy of 1.8 mm. A comprehensive workflow was developed to process the point clouds, including the detection of vegetation and the removal of vegetation-induced point cloud noise. Rasterized and filtered point clouds were then used to determine SSR geostatistically as the standard deviation of height, applying different kernel sizes and using semivariograms. The results showed an influence of kernel size on roughness, with a value range of 0.2–1 cm of average height deviation during the monitoring period. Semivariograms showed a measurable decrease in sill variance and an increase in range over time. This research demonstrated multiple challenges to measuring SSR with UAV under natural conditions with increasing vegetation cover. The proposed workflow represents a step forward in tackling those challenges and provides a knowledge base for future research. © 2020 John Wiley & Sons, Ltd. 相似文献
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Inspired by the idea of the iterative time–frequency peak filtering, which applies time–frequency peak filtering several times to improve the ability of random noise reduction, this article proposes a new cascading filter implemented using mathematic morphological filtering and the time–frequency peak filtering, which we call here morphological time–frequency peak filtering for convenience. This new method will be used mainly for seismic signal enhancement and random noise reduction in which the advantages of the morphological algorithm in processing nonlinear signals and the time–frequency peak filtering in processing nonstationary signals are utilized. Structurally, the scheme of the proposed method adopts mathematic morphological operation to first preprocess the signal and then applies the time–frequency peak filtering method to ultimately extract the valid signal. Through experiments on synthetic seismic signals and field seismic data, this paper demonstrates that the morphological time–frequency peak filtering method is superior to the time–frequency peak filtering method and its iterative form in terms of valid signal enhancement and random noise reduction. 相似文献
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以广播星历为起算轨道的北斗卫星实时滤波精密定轨往往需要较长收敛时间,针对此,提出利用超快速精密星历约束的实时精密定轨方法。通过MGEX跟踪网全球分布的51个测站连续7 d的实测数据,利用平方根信息滤波对北斗卫星实时精密轨道进行确定,并以3 d解事后轨道作为参考,评估北斗卫星实时滤波轨道精度。结果表明,利用广播星历作起算轨道时,北斗实时滤波轨道平均需要经过15 h收敛才能达到稳定,而新方法在这段时间内轨道变化较为平稳,未出现明显的收敛现象,并且7 d时间内GEO卫星在切向、法向和径向上RMS分别优于2.5 m、20 cm和30 cm,IGSO和MEO卫星在3个方向上分别优于30 cm、15 cm和10 cm。 相似文献
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At present, there is less theoretical research and practical experience in the aspect of ultra-shallow seismic exploration to the target layers at depths of only tens of meters both at home and abroad. Seismic exploration plays an important role in the location of faults and active structures, but the depth dozens of meters below the ground surface is the blind area of any kind of deep and shallow seismic exploration. Starting from the point of view of detecting urban active faults, and using related theories and methods of geology, geophysics and mathematics, the paper discusses the preconditions for acquiring efficient ultra-shallow seismic survey results in complicated geological backgrounds in Qingdao. Taking the Qingdao area as an example in this paper, we study the depth condition of Quaternary deposits, and apply 4-8 stacking folds to satisfy the requirement to get the exploration results with high-resolution and high-SNR. Preliminary results reveal that selecting a proper surveillance layout is one of the keys to acquire authentic exploration results in ultra-shallow P-wave reflection exploration. Our results also show that ultra-shallow seismic reflection method in detecting faults in the Qingdao area has good application prospects. 相似文献
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一种基于熵权法的小波去噪复合评价指标 总被引:2,自引:0,他引:2
传统的评价指标在真值未知的情况下不能满足小波去噪质量评价的要求。为此,借助变化率特征重新构建均方根误差变化量和平滑度变化量两个指标,利用熵权法定权将归一化后的两个指标线性组合,所得到的新指标即为复合评价指标。该方法借助指标的变化率随分解层数的增加表现出明显的收敛特性来确定去噪最优分解层数。实验表明,该方法能够在真值未知的情况下准确地指导小波分解,确定去噪最优分解层数,从而达到最优去噪效果。 相似文献
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We reformulate the equation of reverse‐time migration so that it can be interpreted as summing data along a series of hyperbola‐like curves, each one representing a different type of event such as a reflection or multiple. This is a generalization of the familiar diffraction‐stack migration algorithm where the migration image at a point is computed by the sum of trace amplitudes along an appropriate hyperbola‐like curve. Instead of summing along the curve associated with the primary reflection, the sum is over all scattering events and so this method is named generalized diffraction‐stack migration. This formulation leads to filters that can be applied to the generalized diffraction‐stack migration operator to mitigate coherent migration artefacts due to, e.g., crosstalk and aliasing. Results with both synthetic and field data show that generalized diffraction‐stack migration images have fewer artefacts than those computed by the standard reverse‐time migration algorithm. The main drawback is that generalized diffraction‐stack migration is much more memory intensive and I/O limited than the standard reverse‐time migration method. 相似文献