The primary network of groundwater level observation wells aims at realizing a regional groundwater management policy. It may give a regional picture of groundwater level with emphasis on the natural situation. Observ...The primary network of groundwater level observation wells aims at realizing a regional groundwater management policy. It may give a regional picture of groundwater level with emphasis on the natural situation. Observation data from the primary network can be used to estimate the actual state of groundwater system. Since the cost of the installation and maintenance of a groundwater monitoring network is extremely high, the assessment of effectiveness of the network becomes very necessary. Groundwater level monitoring networks are the examples of discontinuous sampling on variables presenting spatial continuity and highly skewed frequency distributions. Anywhere in the aquifer, ordinary kriging provides estimates of the variable sampled and a standard error of the estimate. In this article, the average Kriging standard deviation was used as a criterion for the determination of network density,and the GIS-based approach was analysized. A case study of groundwater level network simulation in the Chaiwopu Basin, Xinjiang Uygur Autonomous Region, China, was presented. In the case study, the initial phreatic water observation wells were 18, a comparison of the three variogram parameters of the three defferent variogram models shows that the Gaussian model is the best. Finally, a network with 55 wells was constructed.展开更多
We propose a new nonparametric approach to represent the linear dependence structure of a spatiotemporal process in terms of latent common factors.Though it is formally similar to the existing reduced rank approximati...We propose a new nonparametric approach to represent the linear dependence structure of a spatiotemporal process in terms of latent common factors.Though it is formally similar to the existing reduced rank approximation methods,the fundamental difference is that the low-dimensional structure is completely unknown in our setting,which is learned from the data collected irregularly over space but regularly in time.Furthermore,a graph Laplacian is incorporated in the learning in order to take the advantage of the continuity over space,and a new aggregation method via randomly partitioning space is introduced to improve the efficiency.We do not impose any stationarity conditions over space either,as the learning is facilitated by the stationarity in time.Krigings over space and time are carried out based on the learned low-dimensional structure,which is scalable to the cases when the data are taken over a large number of locations and/or over a long time period.Asymptotic properties of the proposed methods are established.An illustration with both simulated and real data sets is also reported.展开更多
The primary nugget, first reported in this paper, was discovered in No. 47 branch vein in Dakaitou ore block, Linglong gold deposit, Shandong Province. Thanks to its weight of over 29 kg, exceeding the limit weight (5...The primary nugget, first reported in this paper, was discovered in No. 47 branch vein in Dakaitou ore block, Linglong gold deposit, Shandong Province. Thanks to its weight of over 29 kg, exceeding the limit weight (5 kg) of super large nugget, it is named “Superlarge-Linglong” nugget (Superlarge-Linglong). Observed by either naked eye or microscopy, both the Superlarge-Linglong and adjacent scattered-veinlet visible gold are composed of high-purity (903) native gold, exnclud-ing any other minerals. The Superlarge-Linglong does not cut the other types of gold ore bodies made up of high-purity micro electrum. There is, in other ore bodies and siliceous sericitolite, visi-ble gold with similar purity to it and close space-time and genetic relation to it, indicating that it is younger than other ore bodies and siliceous sericitolite. The Superlarge-Linglong and adjacent visible gold are caused by the independent nugget-visible gold metallogenetic stage.展开更多
基金the National Natural Science Foundation of China (Grant Nos.50579040 and 50570941)
文摘The primary network of groundwater level observation wells aims at realizing a regional groundwater management policy. It may give a regional picture of groundwater level with emphasis on the natural situation. Observation data from the primary network can be used to estimate the actual state of groundwater system. Since the cost of the installation and maintenance of a groundwater monitoring network is extremely high, the assessment of effectiveness of the network becomes very necessary. Groundwater level monitoring networks are the examples of discontinuous sampling on variables presenting spatial continuity and highly skewed frequency distributions. Anywhere in the aquifer, ordinary kriging provides estimates of the variable sampled and a standard error of the estimate. In this article, the average Kriging standard deviation was used as a criterion for the determination of network density,and the GIS-based approach was analysized. A case study of groundwater level network simulation in the Chaiwopu Basin, Xinjiang Uygur Autonomous Region, China, was presented. In the case study, the initial phreatic water observation wells were 18, a comparison of the three variogram parameters of the three defferent variogram models shows that the Gaussian model is the best. Finally, a network with 55 wells was constructed.
基金supported by National Statistical Research Project of China(Grant No.2015LY77)National Natural Science Foundation of China(Grant Nos.11571080,11571081,71531006 and 71672042)+3 种基金supported by Engineering and Physical Sciences Research Council(Grant No.EP/L01226X/1)supported by National Natural Science Foundation of China(Grant Nos.11371318 and 11771390)Zhejiang Province Natural Science Foundation(Grant No.R16A010001)the Fundamental Research Funds for the Central Universities。
文摘We propose a new nonparametric approach to represent the linear dependence structure of a spatiotemporal process in terms of latent common factors.Though it is formally similar to the existing reduced rank approximation methods,the fundamental difference is that the low-dimensional structure is completely unknown in our setting,which is learned from the data collected irregularly over space but regularly in time.Furthermore,a graph Laplacian is incorporated in the learning in order to take the advantage of the continuity over space,and a new aggregation method via randomly partitioning space is introduced to improve the efficiency.We do not impose any stationarity conditions over space either,as the learning is facilitated by the stationarity in time.Krigings over space and time are carried out based on the learned low-dimensional structure,which is scalable to the cases when the data are taken over a large number of locations and/or over a long time period.Asymptotic properties of the proposed methods are established.An illustration with both simulated and real data sets is also reported.
基金This study was supported by the National Doctor Foundation of China(Grant No.97018706)
文摘The primary nugget, first reported in this paper, was discovered in No. 47 branch vein in Dakaitou ore block, Linglong gold deposit, Shandong Province. Thanks to its weight of over 29 kg, exceeding the limit weight (5 kg) of super large nugget, it is named “Superlarge-Linglong” nugget (Superlarge-Linglong). Observed by either naked eye or microscopy, both the Superlarge-Linglong and adjacent scattered-veinlet visible gold are composed of high-purity (903) native gold, exnclud-ing any other minerals. The Superlarge-Linglong does not cut the other types of gold ore bodies made up of high-purity micro electrum. There is, in other ore bodies and siliceous sericitolite, visi-ble gold with similar purity to it and close space-time and genetic relation to it, indicating that it is younger than other ore bodies and siliceous sericitolite. The Superlarge-Linglong and adjacent visible gold are caused by the independent nugget-visible gold metallogenetic stage.
