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11.
Zhiwei Xu James Irving Kyle Lindsay John Bradford Peimin Zhu Klaus Holliger 《Geophysical Prospecting》2020,68(2):678-689
Knowledge about the stochastic nature of heterogeneity in subsurface hydraulic properties is critical for aquifer characterization and the corresponding prediction of groundwater flow and contaminant transport. Whereas the vertical correlation structure of the heterogeneity is often well constrained by borehole information, the lateral correlation structure is generally unknown because the spacing between boreholes is too large to allow for its meaningful inference. There is, however, evidence to suggest that information on the lateral correlation structure may be extracted from the correlation statistics of the subsurface reflectivity structure imaged by surface-based ground-penetrating radar measurements. To date, case studies involving this approach have been limited to 2D profiles acquired at a single antenna centre frequency in areas with limited complementary information. As a result, the practical reliability of this methodology has been difficult to assess. Here, we extend previous work to 3D and consider reflection ground-penetrating radar data acquired using two antenna centre frequencies at the extensively explored and well-constrained Boise Hydrogeophysical Research Site. We find that the results obtained using the two ground-penetrating radar frequencies are consistent with each other, as well as with information from a number of other studies at the Boise Hydrogeophysical Research Site. In addition, contrary to previous 2D work, our results indicate that the surface-based reflection ground-penetrating radar data are not only sensitive to the aspect ratio of the underlying heterogeneity, but also, albeit to a lesser extent, to the so-called Hurst number, which is a key parameter characterizing the local variability of the fine-scale structure. 相似文献
12.
Jesus A. Uriarte Laura Damas Mollá Maialen Sagarna Arantza Aranburu Francisco García Iñaki Antigüedad Tomás Morales 《水文研究》2020,34(4):1004-1015
Locating and quantifying groundwater flow in many built-up areas are a priority with regard to its complete restoration. In this work, a hydrogeological survey of the surroundings of the Punta Begoña Galleries (Getxo, Bizkaia), built on a coastal cliff, was completed by using ground penetrating radar (GPR) testing. Thus, the preliminary characterization of soils and rocks in accessible areas of the cliff was first improved by hydrogeological information gathered from a single survey borehole, including permeability measurements by low pressure injection tests (LPTs) and continuous water level monitoring. As a complementary method, the non-destructive GPR technique was performed during both dry and wet hydrological periods and in tandem with the injection tests, providing more complete spatial and temporal images of water flows. Specifically, GPR allows mapping of flow paths in soils and assessing the continuity of fractures in rock masses. Altogether, this complementary approach provides greater knowledge of complex underground flow dynamics in built environments, thus making it easier to make decisions for their management. 相似文献
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合成孔径雷达差分干涉测量(D-InSAR)技术在多年冻土区地表变形监测中的应用 总被引:2,自引:2,他引:0
多年冻土区地表变形严重影响着区域内生态环境和工程设施的稳定性。合成孔径雷达差分干涉测量(differential interferometric synthetic aperture radar, D-InSAR)技术作为一种新型的空间对地观测技术, 为多年冻土区地表变形监测提供了新方法。通过近20年的不断深入研究, 利用D-InSAR技术的多年冻土区地表变形监测取得了大量研究成果。首先介绍了D-InSAR技术测量地表变形的理论基础, 进而概述了D-InSAR技术在多年冻土区地表变形测量中的应用现状, 然后总结了D-InSAR测量过程中存在的关键问题及可能的解决方法。在此认识和分析的基础上, 对D-InSAR技术今后在多年冻土区地表变形监测中的发展方向进行了探讨, 以期为D-InSAR技术在多年冻土区地表变形监测中的推广应用和深入研究提供参考。 相似文献
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平原地区机载激光雷达数据的抽稀算法分析 总被引:1,自引:0,他引:1
目前,机载激光雷达点云数据在测绘行业中的应用还存在较多的瓶颈。为了使机载激光雷达点云数据更好地服务等值线等数据的生产,发挥其高效和高精度的优势,本文归纳、总结了国内外现有的LiDAR点云数据抽稀算法,并通过对比分析现有LiDAR点云数据抽稀算法存在的优缺点,如系统抽稀、格网抽稀、TIN抽稀和坡度抽稀等算法,结合平原地区激光点云在实际生产中的应用,研究了更适合平原地区点云数据的抽稀方法,通过大量的数据测试和试生产。结果表明,该方法可以在应用项目精度约束下保证数据质量,减少了后期数据处理应用的难度,提升了后续成果数据的质量,提高了作业生产效率,对机载激光雷达点云数据在测绘行业中的应用推广具有重要的现实意义。 相似文献
16.
The objective of this study is to efficiently extract detailed information about various man-made targets in oriented built-up areas using polarimetric synthetic aperture radar (POLSAR) images. This paper develops an improved approach for building detection by utilizing Two-Dimensional Time-Frequency (2-D TF) decomposition. This method performs outstandingly in distinguishing between man-made and natural targets based on the isotropic behaviors, frequency-sensitive responses, and scattering mechanisms of objects. The proposed method can preserve the spatial resolution and exploit the advantages of TF decomposition; specifically, the exact outlines of buildings can be effectively located, and more types of features (e.g., flat roofs, roads, and walls that are oblique to the radar illumination) can be distinguished from forests in complex built-up areas by 2-D TF decomposition. The coarser-resolution subaperture images that are produced in the azimuth direction, which correspond to different looking angles, are beneficial for detecting man-made structures with main scattering centers oriented at oblique angles with respect to the radar illumination. In the range direction, the obtained subaperture images, which correspond to various observation frequencies, can be helpful in distinguishing flat roofs and roads from forests. This method was successfully implemented to analyze both NASA/JPL L-band AIRSAR and L-band EMISAR data sets. The building detection results of the proposed method exhibit a significant improvement over those of other methods and reach an overall accuracy over 80%, with approximately 20% higher than the accuracies of K-means clustering and the entropy/alpha-Wishart classifier and approximately 10% higher than the accuracy of the support vector machine method. Moreover, building details can be precisely detected, obliquely oriented buildings can be identified, and the distinction between buildings and forests is significantly improved, as both visually and statistically indicated. This method is highly adaptable and has substantial application value. 相似文献
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Speckle noise in synthetic-aperture radar (SAR) images severely hinders remote sensing applications; therefore, the appropriate removal of speckle noise is crucial. This paper elaborates on the multilayer perceptron (MLP) neural-network model for SAR image despeckling by using a time series of SAR images. Unlike other filtering methods that use only a single radar intensity image to derive their parameters and filter that single image, this method can be trained using archived images over an area of interest to self-learn the intensity characteristics of image patches and then adaptively determine the weights and thresholds by using a neural network for image despeckling. Several hidden layers are designed for feedforward network training, and back-propagation stochastic gradient descent is adopted to reduce the error between the target output and neural-network output. The parameters in the network are automatically updated in the training process. The greatest advantage of MLP is that once the despeckling parameters are determined, they can be used to process not only new images in the same area but also images in completely different locations. Tests with images from TerraSAR-X in selected areas indicated that MLP shows satisfactory performance with respect to noise reduction and edge preservation. The overall image quality obtained using MLP was markedly higher than that obtained using numerous other filters. In comparison with other recently developed filters, this method yields a slightly higher image quality, and it demonstrates the powerful capabilities of computer learning using SAR images, which indicate the promising prospect of applying MLP to SAR image despeckling. 相似文献
20.
Measurement of stream cross section using ground penetration radar with Hilbert–Huang transform 下载免费PDF全文
This study presents a new method to measure stream cross section without having contact with water. Compared with conventional measurement methods which apply instruments such as sounding weight, ground penetration radar (GPR), used in this study, is a non‐contact measurement method. This non‐contact measurement method can reduce the risk to hydrologists when they are conducting measurements, particularly in high flow period. However, the original signals obtained by using GPR are very complex, different from studies in the past where the measured data were mostly interpreted by experts with special skill or knowledge of GPR so that the results obtained were less objective. This study employs Hilbert–Huang transform (HHT) to process GPR signals which are difficult to interpret by hydrologists. HHT is a newly developed signal processing method that can not only process the nonlinear and non‐stationary complex signals, but also maintain the physical significance of the signal itself. Using GPR with HHT, this study establishes a non‐contact stream cross‐section measurement method with the ability to measure stream cross‐sectional areas precisely and quickly. Also, in comparison with the conventional method, no significant difference in results is found to exist between the two methods, but the new method can considerably reduce risk, measurement time, and manpower. It is proven that the non‐contact method combining GPR with HHT is applicable to quickly and accurately measure stream cross section. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献