Slant-path water vapor amounts (SWV) from a station to all the GPS (Global Positioning System) satellites in view can be estimated by using a ground-based GPS receiver. In this paper, a tomographic method was utilized to retrieve the local horizontal and vertical structure of water vapor over a local GPS receiver network using SWV amounts as observables in the tomography. The method of obtaining SWV using ground-based GPS is described first, and then the theory of tomography using GPS is presented. A water vapor tomography experiment was made using a small GPS network in the Beijing region. The tomographic results were analyzed in two ways: (1) a pure GPS method, i.e., only using GPS observables as input to the tomography; (2) combining GPS observables with vertical constraints or a priori information, which come from average radiosonde measurements over three days. It is shown that the vertical structure of water vapor is well resolved with a priori information. Comparisons of profiles between radiosondes and GPS show that the RMS error of the tomography is about 1–2mm. It is demonstrated that the tomography can monitor the evolution of tropospheric water vapor in space and time. The vertical resolution of the tomography is tested with layer thicknesses of 600 m, 800 m and 1000 m. Comparisons with radiosondes show that the result from a resolution of 800m is slightly better than results from the other two resolutions in the experiment. Water vapor amounts recreated from the tomography field agree well with precipitable water vapor (PWV) calculated using GPS delays. Hourly tomographic results are also shown using the resolution of 800 m. Water vapor characteristics under the background of heavy rainfall development are analyzed using these tomographic results. The water vapor spatio-temporal structures derived from the GPS network show a great potential in the investigation of weather disasters. 相似文献
Halocyprid ostracods are appreciable part of ostracods floating through virtually everywhere in marine environment.In this study,we describe a new species of genus Polyconchoecia Xiang,Chen and Du,2018,tribe Conchoeciini Chavtur and Angel,2011,family Halocyprididae Dana,1853 from the middle of the South China Sea.Polyconchoecia chenii sp.nov.is very close to P.commixtus Xiang,Chen and Du,2018.But it differs from P.commixtus by the distinctions of locations of major glands of carapace and the characteristics of appendages:more posteriorly situated left asymmetric gland of carapace,no right asymmetric gland;segmented frontal organ;the endopod 2 of the first antenna with a very small seta;a-and c-setae of the first antenna with long end joint have long end joint,the b-and d-setae have no end joint,spinose e-seta without end joint;the e-seta of the second antenna is present;teeth side is distinctive;the setal counts of the mandible,maxilla,fifth limb,and sixth limb are individual.The locations of the major glands on carapace and the characteristics of the first antenna can be the key of the new species.This work is the second discovery of the genus Polyconchoecia from the world. 相似文献
China Ocean Engineering - In order to reduce the hydrodynamic and structural influences on the detection accuracy especially in the very-low-frequency range, some vibration restraint methods are... 相似文献
Exploring the spatial relationships between various geological features and mineralization is not only conducive to understanding the genesis of ore deposits but can also help to guide mineral exploration by providing predictive mineral maps. However, most current methods assume spatially constant determinants of mineralization and therefore have limited applicability to detecting possible spatially non-stationary relationships between the geological features and the mineralization. In this paper, the spatial variation between the distribution of mineralization and its determining factors is described for a case study in the Dingjiashan Pb–Zn deposit, China. A local regression modeling technique, geological weighted regression (GWR), was leveraged to study the spatial non-stationarity in the 3D geological space. First, ordinary least-squares (OLS) regression was applied, the redundancy and significance of the controlling factors were tested, and the spatial dependency in Zn and Pb ore grade measurements was confirmed. Second, GWR models with different kernel functions in 3D space were applied, and their results were compared to the OLS model. The results show a superior performance of GWR compared with OLS and a significant spatial non-stationarity in the determinants of ore grade. Third, a non-stationarity test was performed. The stationarity index and the Monte Carlo stationarity test demonstrate the non-stationarity of all the variables throughout the area. Finally, the influences of the degree of non-stationary of all controlling factors on mineralization are discussed. The existence of significant non-stationarity of mineral ore determinants in 3D space opens up an exciting avenue for research into the prediction of underground ore bodies.
