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The current groundwater monitoring network has many problems caused by the history and poor techniques, which has provided incomplete groundwater information and it is adverse to the management of groundwater resources. So it is necessary to optimize it. The writer takes the deep pore confined water monitoring network in Ningbo Plain as an example ,using the main two parts of geo-statistics—variogram and Kriging interpolation to adjust the number and position of the current 29 wells on it, finally getting a new more reasonable monitoring network consists of 19 wells after removing 17 redundant wells and adding 7 new wells. Foreword The data of groundwater regime can reveal its flow law, help the decision makers manage groundwater resources effectively and guarantee the sustainable utilization of groundwater resources, so it’s very important to make a economical and high efficient monitoring network for acquiring data of groundwater regime. With the urbanization process keep going, the current monitoring networks expose some problems which can not help to get complete and accurate data and this will impede the development of economic social, needing to be optimized right now. All of the problems of the current well network, the main is that the layout of wells is not very reasonable. Fortunately, the geo-statistics set up by the French statisticians G.Matheron can well resolve such problem, and which has been used in many successful cases. The paper just use this theory to optimize the spatial layout of wells on Ningbo Plain’ deep pore confined aquifer, and the optimal solution. Principle of Geo-Statistics Geo-statistics is science which is based on the theory of regionalized variable, taking variogram as the basic tool and using Kriging method to research natural phenomenon which both have randomness and constitutive property in spatial distribution. It consists of Kriging interpolation and variogram. Kriging Interpolation. Kriging interpolation is mainly used to make optimum linear unbiased estimation of regionalized variable in limited area. Assuming that there have gotten N observed values Z(xi)(i=1~N) of regionalized variable Z(x) in a region, then, the value of any unknown point x0 in this region can be estimated by the following equation, 0 1 *( ) ( ) N i i i Z x Z x = =λ (1) Where, 0 *( ) Z x is estimated value of point x0 calculated by Z(xi), i λ is the Kriging weight coefficient. Combining with the definition of covariance, under the unbiased and best conditions , through bringing in Lagrange algorithm ,using eq.1 to estimate, which can deduce Kriging equation set as following, 2nd International Conference on Science and Social Research (ICSSR 2013) © 2013. The authors Published by Atlantis Press 96

Optimization Design of Groundwater Level Monitoring Network Based on the Geo-Statistics