2018 Vol. 43, No. 9
Due to the geodynamic factors, especially the earth's surface atmosphere, surface water and groundwater hydrodynamic environment, ground site location, earth gravity field and geoid level change over time. Based on the Three Gorges CORS stations network and a small number of gravity stations, the crustal deformation and monthly gravity change of the Three Gorges area from January 2011 to June 2015 are calculated and analyzed in this paper by combination of load deformation and gravity field. The results show that:(1) CORS network has the ability to monitor the vertical deformation of the crust, ground gravity and geoid change. (2) The annual variation of the crustal vertical deformation in the Three Gorges area is 36.1 mm, the annual variation range of the geoid is 28.2 mm, and the annual variation of ground gravity is 117.4 μGal. (3) The accuracy of ground gravity change monitoring level of CORS network is not lower than the accuracy of flow gravity field. (4) Vertical deformation and ground gravity change monitoring has a certain ability of extrapolation forecasting.
In order to improve the accuracy and reliability of surface subsidence prediction of underground mining, a surface subsidence prediction method of underground mining based on HIOA and MK-RVM is proposed. First, the HIOA and MK-RVM algorithms are constructed, and the parameters of MK-RVM are optimized by using HIOA. Then, the prediction model of the surface subsidence geometric parameters and the dynamic subsidence prediction model are constructed based on the optimized MK-RVM. Finally, based on the above model, the rise moving angle, dip moving angle, central moving angle, maximum subsidence and the dynamic subsidence are predicted. The accuracy and reliability of the prediction results are analyzed in order to verify the effectiveness of the proposed method. Experimental results show that the accuracy and reliability of this method are better than single kernel correlation vector machine and support vector machine, and the accuracy and reliability of the new method are excellent. The above analysis confirms the effectiveness of the new method.
Atmospheric pressure loading needs to be considered due to the great impact on the proce-ssing of high precision geodetic observations data and related geophysical interpretation. In this study, we use the global atmospheric model and regional atmospheric pressure data on weather stations, and calculate the impact of atmospheric pressure loading on crustal deformation and gravity change in the Three Gorges region of China on the basis of the remove-restore method and spherical harmonic analysis method. The proposed method can effectively solve global-scale model data being applied to the region, to improve the utilization of local data and global pressure model. This study finds that the impact of atmospheric pressure loading on the vertical deformation in the spatial distribution is mainly on the long wave. The amplitude of annual variation is greater than 20 mm in the Three Gorges region. The magnitude of gravity change on the ground is generally not more than 10 μGal. However, the impact on the horizontal deformation is very little. The results of this study have important reference value for separating the atmospheric effects in the processing of GNSS data and gravity data, and for analyzing.
The data of sensor geophysical data record(SGDR) of Jason-2 altimeter was collected in China coastal region, and four parameters maximum likelihood estimation method was used for waveform retracking, which can be applied for determinating the mean sea surface.The high frequency correction signals between adjacent observations along the track direction of the altimetry satellite Jason-2 SGDR(sensor geophysical data) record data are correlated near the offshore region. Based on this character, the method for optimal selection Gaussian low pass filter radius is put forward. Firstly, difference calculation is needed between the adjacent observations along the track direction. Secondly, form diffe-rence sequence data sets. Thirdly, determinate the filter radius according to correlation coefficient of the sequences. If the user wants to filter the SGDR data, it is best to select the filter radius of the Gaussian filter equals to 2 km. This method not only can suppress the high frequency error in the SSH(sea surface height), but also can ensure that the data is not excessively smooth. It can be considered as a reference for the establishment of high precision sea surface height model by making full use of satellite altimetry data in offshore region, and also can promote the construction of high precision sea seamless vertical datum.
The survey line layout is the primary content of the technique design in marine gravity survey, which has decisive effect on assurance of the survey precision and efficiency. Survey line layout involves two aspects:One is the choice of line direction; the other is the determination of line spacing. The line direction should be as perpendicular as possible to the direction of the gravity anomaly contour; the line spacing should be widened as much as possible to meet the requirements of the accuracy of the results, in order to reduce the workload and improve the measurement efficiency. Given no sufficient consideration to the characteristics of the gravity anomaly, the line spacing is decided by survey assignment at present. The precision of EGM2008 model gravity anomaly is high in marine area; it can be used as priori information for the layout of marine gravity measurement lines. Based on the characteristics of marine gravity measurement, the EGM2008 model is used to estimate the spacing of marine gravimetric survey lines under the premise of meeting the accuracy requirements. The measured data is further used to verify the reasonableness of the survey line spacing layout. The results show that applying EGM2008 model to estimating the line layout spacing can meet accuracy requirements in marine gravity survey, which can provide the quantitative index for the optimizing of the survey line layout.
For three-dimensional coordinate transformation, it's impossible using the Gauss-Markov model to obtain optimal parameter estimation from the functional model with error in its coefficient matrix. On the other hand, errors-in-variables model has difficulty expressing the functional model, and partial errors-in-variables model is complex as well as too much parameters to be estimated for the quasi-observation method. Therefore, Gauss-Helmert model is employed for three-dimensional coor-dinate transformation. The target function of the proposed model is established based on Newton-Gauss iterative algorithm, and the estimated method and its derivation procedure also are presented in this paper. Beyond the above process, we proposed a new robust estimation method for the proposed model, which is based on the normalized residual error and takes the influence of gross error on both observation and structure spaces into consideration. Meanwhile, derivational process of statistical tests and iterative algorithm are presented. The simulation experiment results show that the proposed estimation method has the same accuracy as the traditional method, which has robust with angular dimension and other additional conditions, but less estimation parameters. In addition, the new robust estimation method has effective robustness when comparing with the other existing robust total least square methods for the coordinate transformation.
The relationship model between the GPS multipath interferogram and soil moisture is a cri-tical factor for precise soil moisture monitoring. The traditional strategy is based on the linear model by collecting as many normal samples as possible during eliminating the outliers, however, it pays little attention to the factors which change slowly but affect the reflection environment such as slope, vegetation and weather. As the changes of these factors could be ignored in a short term, the time window is introduced. At first the window length is determined with correlation analysis, then the dynamic prediction and interpolation model could be realized by linear regression with samples within the window. The test results with real GPS, soil moisture and weather data show that, the prediction and interpolation error are reduced by 17.4% and 54.6%, and the correlation efficient are increased by 16.2% and 32.9% respectively. The interpolation is more accurate than prediction owing to the future samples, while the prediction model could be applied in real applications. The residual analysis show that the correlation between the epoch of maximal residue and soil moisture fluctuation exists. The maximal prediction residue is slightly weak and prior to the rises of soil moisture.
In this paper, a total least squares joint (TLS-J) adjustment method is proposed to the inversion of Mogi model with vertical and horizontal observational data. The proposed method considers the errors in both observation vector and coefficient matrix of the functional model of joint adjustment problem. Three forms of the minimum discriminate function methods are adopted to determine the weight scaling factor which are used to weigh the vertical and horizontal observation data. In view of the existing ill-posed problems in the joint adjustment, the L-curve method is adopted to determine the ridge parameter. Through practical examples, the total least squares joint method is systematically applied to the inversion of the Mogi model of Changbaishan Tianchi volcano. The results show that the discriminant function as the minimum can obtain the reasonable value of pressure source parameters and the relative weight ratio, which has a certain referential value to practical applications.
BeiDou is developed by China independently, the distribution and number of its reflection events have special advantages, which is the important indicator of LEO-R(low earth orbit-reflection)constellation. By using the Walker architecture which has the advantage of the uniformity and stability to design LEO-R constellation, it can realize the global ocean remote sensing based on BeiDou reflected signals. The geometric relation between the constellation and BeiDou is analyzed and the judgment condition of the reflection events is also derived. The impacts of constellation parameters on the number and distribution of the BeiDou reflection events are quantitatively analyzed by proposing mark-traverse algorithm, at the same time, the chosed simulation scene is 36 hours Nepartak typhoon. The results show that the percentage of constellation coverage time is 97.619 73% and the maximum revisit time is 404.511 s, which validates the constellation good performance on the global remote sensing.
There usually exists some prior information with inequality constraint in the survey of adjustment model. The uniqueness and stability of the solution can be guaranteed by making full use of it. However, the existing adjustment algorithms with inequality constrain, which are mainly based on optimization theory, are usually complex. They need to select the effective constraint or establish penalty function. This paper mainly studies the adjustment model with inequality constraint, in which the inequality constraint is considered as a feasible region on the basis of the least squares adjustment rule and the Fisher function is used to search the optimal solution that minimizes the sum of squared errors, and sufficient necessary conditions for the optimal feasible solution are derived. A non-precise fast search based on Wolfe-Powell algorithm is given in the feasible region, which reduces the computational complexity, a new adjustment algorithm with inequality constraint is presented. The given algorithm, in which the adjustment criterion is consistent with that of the least squares adjustment criteria, does not require matrix inversion operation, and can solve some of the large dimension adjustment problem with inequality constrain.
Current solutions of configuration of ground-based pseudolite navigation system cannot meet the emergency application demand because their time-complexity is too high and the results are not optimal enough. To solve this problem, an algorithm named optimization searching based on maximum convex-hull (OSMC) is proposed. In OSMC, all location points which pseudolites can be placed on are evaluated according to the maximum convex hull theory, the result of evaluation is the importance of a location point. The more important a location point is, the higher scores it can get. After all location points are evaluated, we have an overall result of all location points. From the overall result we can find out the location points which are suitable for being set up as bordering pseudolites or central pseudolites, in the global optimum constellation. When the points having lower scores are excluded, the rest points would have the higher probability to constitute the best constellation. Exhaustive method is used in the remain points to search the best constellation. OSMC can be used in irregular areas to get a quasi-optimal pseudolite distribution rapidly. The simulations of OSMC in practical examples confirm that OSMC can truly get the quasi-optimal constellation in much shorter time than the other current solutions, and the results are optimal enough, which meets the demand of emergency applications.
According to the fact that InSAR has the advantages of the high spatial resolution while GPS has the advantages of the high precision and high time resolution, we discuss the method of obtaining high precision three-dimensional deformation field by integrating both ascending and descen-ding orbits InSAR data and GPS data. To weaken the influence of the remaining systematic errors in InSAR data, an integrated deformation analysis model with additional systematic parameters is established, where systematic error functions related to the position of observed points are added in both ascending and descending orbits InSAR observation equations under the constraint of high accurate GPS observations. The simulated and practical examples show that the proposed method can identify systematic error in InSAR data effectively and the calculation is simple. As a result, the three-dimensional deformation field with a higher accuracy will be established by the method proposed in this paper.
In this study, we construct high-quality three-dimensional coseismic surface deformation field of the 2007 Ali earthquake using ascending and descending SAR data acquired from Envisat sate-llite with constraint of earthquake rupture model. Firstly, we use two-pass differential interferometry to generate line-of-sight coseismic surface deformation field of the earthquake from two pairs of SAR images. Then, with constraint of three components of displacement derived from the earthquake rupture model, we obtain the high-quality three-dimensional coseismic surface deformation field using Helmert variance component estimation method. The results show that the subsidence caused by the event is about 4.7 cm in the epicenter, where east-west displacement is small and the epicentral area is under north-south compression. We also calculate quasi east-west and quasi north-south displacements only with InSAR observations, whose patterns of deformation distribution are consistent with the results constrained by USGS earthquake rupture model. Finally, the characteristics of the three-dimensional deformation field indicate that the event is dominated by normal motion with slightly right-lateral strike-slip component.
Considering that the low accuracy of extracted point features may affect the seamless fusion of point clouds from two neighbor stations, and by using traditional iterative-form solutions to implement point clouds registration, the large amount of computer resources, the high dependence on initial values of unknown parameters, and its theoretical instability in solving transformation parameters for large-angle registration can hardly be neglected. To alleviate the above problems, a linear features-based, closed-form solution to registration of pairwise terrestrial LiDAR point clouds is proposed, in which Plücker coordinates is introduced to represent linear features in 3D space. A Plücker coordinate-based object function is first introduced on the assumption of the consistency of each conjugate linear features from the two neighbor stations after registration. Based on the theory of least squares and by extreme value analysis of the error norm, detailed derivations of the model and the main formulas are all given. Experiments show that the proposed algorithm is just the one expected, the linearization of multivariate function is neglected in the implementation, and it runs well without initial estimates of unknown parameters, which assures the stability in solving transformation parameters for large-angle registration problems. Furthermore, by employing linear features as registration primitives, random errors may be greatly decreased by fitting contrast to point features based registration algorithms.
Phase unwrapping is one of the key processing steps during reconstruction the digital elevation model from the signal of interferometric synthetic aperture radar(InSAR) or interferometric synthetic aperture sonar(InSAS). In order to solve the problem of low unwrapping efficiency with large interferogram, we propose a blocked minimum discontinuity phase unwrapping algorithm in shared memory environment. The whole phase quality map is computed firstly through multi-cores of CPU, then wrapped phase image and phase quality map are tessellated into small regular blocks, which are unwrapped by the quality guided and minimum discontinuity optimization method simultaneously. In the end, the minimum discontinuity optimization process is performed again on the border of different blocks and the low quality areas located on the border to get the final merged unwrapped result in the main thread. Tests performed on the simulated and real InSAS interferogram show that the speed up reaches 3.98 and 2.26 respectively.
Driven by the miniaturization, lightweight of positioning and remote sensing sensors as well as the urgent needs for fusing indoor and outdoor maps for next generation navigation, 3D indoor mapping from mobile scanning is a hot research and application topic. It has been applied in indoor modeling, indoor localization and other rising fields. In general, 3D indoor mobile mapping systems are equipped with sensors include laser scanner, panoramic camera, IMU (inertial measurement unit) system and odometry. The IMMS system can achieve indoor 3D indoor point cloud data acquisition, but the range sensor, laser scanner, is very costly and poor portability. RGB-D (RGB-depth) camera can offer an alternative way to capture data from indoor scene. However, the narrow field of view of RGB-D sensors cannot provide sufficient efficiency and wholeness of data acquisition, and may cause tracking failed or false match more frequently in SLAM system. This paper builts a low cost indoor 3D mobile mapping system prototype device, and proposes a calibration approach which integrates multiple consumer level RGB-D cameras to large field of view system to solve the deficiency mentioned above. The overall analysis shows the precision of this system meets the demanding of basic application of indoor data collection.
The spatial and temporal characteristics of human activities are closely related to the function of urban land use, so the social-economic function of the urban parcel can be inferred by the spatial aggregation and dispersion of human activities. Cell phone is the most popular communication terminal equipment and the distribution of cell phone users is able to reflect the distribution of population accurately. Local basic service (LBS), which is acquired from residents' cell mobile data, is constantly emerging and make it possible to achieve spatial and temporal coverage and meticulous monitoring of urban people's activities. Therefore, the mobility data of cell phone users have the potential to infer the land use function of the urban parcels. In this paper, the call detail records(CDRs), will be adopted to cluster the urban land use patterns. Firstly, the clustering characteristics of call aggregation for local scale are extracted, then a spectral clustering recognition method for urban land use is proposed. Taking Wuhan as an experimental area, the average accuracy of the method for urban land use identification is 54.6%, and the results show that this method has advantages in urban land use identification.
The development of universal public map service is the milestone for the coming of digital life and smart city. And how to detect the group-user access patterns and how to map the virtual access behaviors into real world behaviors precisely and quickly is the key to accelerate the development of the public map service and the construction of smart city. This paper researches the temporal and spatial characteristics of group-user access hotspots in public map service. Based on the massive user access logs and using three statistical methods of group analysis, time series analysis and three-dimensional visualization, the research results show the group-user access hotspots have the characteristics of week periodicity, and most hotspots appear constantly within one period. Moreover, the boxplots and density diagrams of access hotspots distance show that a large amount of the hotspots are gathered within short distance while much less hotspots are far apart. And the spacing distances distribution differs in different map layers. The temporal and spatial characteristics of hotspots in public map service reveal the behavioral intention behind the massive data of user access, it digitizes the real world behaviors, which improves the man-earth relationship in smart city.
The location-based service requires fast query, insertion and deletion operations for research objects, and this demand is augmented for the indoor evacuation fields. Thus, introducing spatial index to tackle the operation efficiency problem is strongly demanded for indoor related scenes, and this method is sounding for indoor spatial objects. Nevertheless, this solution always meets a performance bottleneck. And this performance bottleneck of spatial index pervasively exists in the current compact indoor application scenes. To mitigate this problem, this paper tries to combine the R* tree index with the Hilbert curve, and proposes an innovative Hilbert curve based index. The succeeding experiment is designed to compare the performance of proposed index with the classic R* index. The test result shows the new index has successfully alleviated the execution efficiency for multi-type spatial operations, especially on critic indicators of spatial index performance.
Existing spatial scene matching methods try to use low level features such as local geome-trical shape descriptors, binary spatial relations of cardinal directions or topology and so on to meet with the high level description demands from users, and lack the feedback mechanism of user subjective concepts. This paper applies the feedback relevance mechanism to spatial scene matching, analyzes the effects after studying users' needs, and then evaluates the relevance of matching results to the desired spatial scene. According to the feedback, the retrieval model updates the weights of descriptors to simulate user's perception, as a result, the retrieval fits the users' requirements much better. The experiment verifies the efficiency and feasibility by users survey and weights convergence. Experimental result shows that spatial scene matching based on multilevel relevance feedback has a high user subjectivity and accords with human's cognizance. In addition, users could obtain satisfactory result by two or three iterations.
In order to solve the problem of the poor accuracy of Web map point symbol user interest during the process of Web map personalized recommendation, we proposed a method for calculating user interest degree of Web map point symbols based on eye movement data. Using mental cutting test, 39 subjects with similar cognitive ability were selected to participate in the experiment and thus we collected subjects' eye movement and mouse data in four types Web map point symbols. We filtered time, frequency and size eye movement data to calculate user interest degree, and established a new method for calculating user interest degree based on multiple eye movement data. An experiment using eye-tracking and mouse device was designed to verify the effectiveness of the method. The results indicate that the accuracy of user interest degree is 85.9%, which is better than those of mouse data. It has been proved that this method is able to effectively analyze the user interest degree, and that the user interest formula is stable and reliable, which lays the foundation for personalized recommendation and improves the effectiveness of recommendation results.
In this paper, taking the maximum absolute error for vector data into account, we propose an approximate DCT compression method, which is designed for controlling the maximum absolute error and the high arithmetic complexity, aimed at traditional local compression algorithm for vector data based on discrete cosine transform. First of all, we construct vector data blocks on the basis of vector data topology. Secondly, we calculate the optimization solution of the agreed matrix in accor-dance with the orthogonality of approximate DCT transform, and then set the solution which has the minimum computational complexity as the conversion matrix for approximate DCT transform to ma-ximally guarantee the precision of vector data. Finally, we combine the total energy between the DCT transform and accurate approximate DCT transform of vector data with maximum absolute error of the reconstructed data, and use three spline interpolation function for data which exceeds thresholds of error to ensure maximum accuracy. Experimental results show that the proposed method has low computational complexity and high speed compression, it can keep the topological relation of the spatial data and the accuracy of the data while reducing the compression rate.