When groundwater pollution occurs, to come up with an efficient remediation plan, it is particularly important to collect information of contaminant source (location and source strength) and hydraulic conductivity field of the site accurately and quickly. However, the information can not be obtained by direct observation, and can only be derived from limited measurement data. Data assimilation of observations such as head and concentration is often used to estimate parameters of contaminant source. As for hydraulic conductivity field, especially for complex non-Gaussian field, it can be directly estimated by geostatistics method based on limited hard data, while the accuracy is often not high. Better estimation of hydraulic conductivity can be achieved by solving inverse groundwater problem. Therefore, in this study, the multi-point geostatistics method Quick Sampling (QS) is proposed and introduced for the first time and combined with the iterative local updating ensemble smoother (ILUES) to develop a new data assimilation framework QS-ILUES. It helps to solve the contaminant source parameters and non-Gaussian hydraulic conductivity field simultaneously by assimilating hydraulic head and pollutant concentration data. While the pilot points are utilized to reduce the dimension of hydraulic conductivity field, the influence of pilot points' layout and the ensemble size of ILUES algorithm on the inverse simulation results are further explored.

Identification of the location and intensity of groundwater pollution source contributes to the effect of pollution remediation, and is called groundwater contaminant source identiication. This is a kind of typical groundwater inverse problem, and the solution is usually ill-posed. Especially considering the spatial variability of hydraulic conductivity field, the identification process is more challenging. In this paper, the solution framework of groundwater contaminant source identification is composed with groundwater pollutant transport model (MT3DMS) and a data assimilation method (Iterative local update ensemble smoother, ILUES). In addition, Karhunen-Loève expansion technique is adopted as a PCA method to realize dimension reduction. In practical problems, the geostatistical method is usually used to characterize the hydraulic conductivity ield, and only the contaminant source information is inversely calculated in the identiication process. In this study, the identiication of contaminant source information under Kriging K-field is compared with simultaneous identification of source information and K-field. The results indicate that it is necessary to carry out simultaneous identification under heterogeneous site, and ILUES has good performance in solving high-dimensional parameter inversion problems.

The slope instability and uneven settlement caused by the foundation pit dewatering pose a great threat to the project and surrounding environment. Thus, it is necessary to carry out optimization design of the foundation pit dewatering project. To solve this problem, this paper introduces the simulation-optimization method and establishes the groundwater model based on MODFLOW-2005. The local grid refinement (LGR) technique is employed to achieve the refinement of dewatering area. Under the premise of construction safety, the minimum cost of the project is set as the objective function to solve the optimization problem of foundation pit engineering with GWM-2005. The results show that the optimization with GWM-2005 will be more accurate combined with the LGR model, but the improvement of the optimization is not obvious. It is necessary to choose a suitable local refinement model considering the engineering requirement.

This paper targets its research at the exploitation–reinjection well of geothermal fluid of one geothermal heating project in Tianjin, China, examines such factors as ground temperature, CO₂ partial pressure and stratum lithology, and simulates the changes in the main component contents of geothermal fluids mixed at different proportions in the exploitation and reinjection well. The research findings show that the mixed fluids are increasingly similar in nature to the reinjected water as the reinjection process goes on. It’s suggested that the manual method should be used to ensure the reinjected water has the similar mineralization as the exploited ground water in the process of reinjection and some acceptable adjustments should be made according to the specific component and water temperature. The study on water-rock balance calculation shows that PHREEQC can simulate the complicated chemical reactions related to water when the transfer of solute happens, so the necessary technological supports are given for the reasonable development and protection of geothermal resources.