Study On Water Migration Characteristics Of Soil Based On Nuclear Magnetic Resonance Technology

With the accelerated development of industrialization and urbanization in China,the production of urban solid waste has been increasing,making it necessary for cities to increase their efforts in addressing the challenges of waste recycling and disposal.Landfill is one of the most common methods of waste disposal.During the degradation process,waste releases heat,causing the temperature of the surrounding soil to rise.Water molecules in the soil undergo migration under the coupling effect of water,heat,and force,resulting in the transfer of leachate from the landfill to groundwater,causing serious groundwater pollution.Compared with other countries,the research on landfill permeability in China started late and there is a lack of research on real-time and nondestructive monitoring of water migration in soil under temperature gradient driven by microstructure methods.Therefore,it is of great scientific significance and engineering value to illuminate the mechanism of water migration under temperature gradient driving for the normal operation and long-term stability of landfills.In this study,the advanced microstructure detection technology（NMR）was used to obtain the pore distribution information of the soil,and the NMR signals of soils with different initial water contents were tested,and the relationship between NMR signals and water content was summarized.The pore size distribution of soils with different dry densities was also tested,and a permeability coefficient prediction model considering pore size and distribution was established.The changes of water migration under temperature gradient driving were tested for soils with different initial water contents,and the microscopic mechanism of water migration was revealed.The main research results obtained in this study are as follows:（1）The character of pore water content and distribution status of different positions in soil samples were analyzed based on the latest SFG-MSCPMG pulse sequence of NMR in a fast and real-time quantitative manner.There is a good linear relationship between the total NMR signal and the water content of the effective slice,that is,the NMR signals can accurately characterize the water content of sliced soil.When the transverse surface relaxation relaxivity parameterρ₂=41.90μm/s of clay A is known,the T₂time distribution curve shows that the optimal pore water diameters corresponding to the soil moisture content of 2.93%,4.49%,7.27%,11.02%,13.48%,and 15.27%are 0.13μm,0.31μm,0.50μm,0.69μm,0.82μm,0.82μm,respectively.It is more suitable for using T₂corresponding to the peak point to describe the average pore size of pore water in the expansive soil.（2）A model for rapid prediction of the permeability coefficient of soil using T₂distribution is developed based on the advantages of nuclear magnetic resonance（NMR）in quickly determining pore size distribution.With the permeability coefficient of a certain dry density soil and the T₂distribution curves of different dry density soils,the model is able to estimate the permeability coefficient of soil under any dry density condition.The research results show that the capillary model is highly efficient for estimating the permeability coefficient of soil at different dry densities since it does not need to calculate the transverse surface relaxivityρ₂;The predicted results of the model are basically consistent with the measured values,indicating that the method is fast,reliable and unaffected by human behaviors.（3）Under the driving force of temperature gradient,when the water distribution in the soil is stable,the water distribution pattern of soil samples with different initial water content varies greatly.Regardless of the stage of soil water content,water molecules migrate from the high temperature end to the low temperature end under the driving force of temperature gradient.The effect of water migration is obvious in the low water content state,while there is almost no water migration in the medium-high water content states.