Soil salinity simulation based on electromagnetic induction and deep learning

Abstract

In pursuit of fine modeling and forecasting of water-salt transport in arid zones, this study uses the rapid electromagnetic induction survey as the main tool to establish an empirical relationship between soil volumetric water content, salinity and electrical conductivity based on on-site measurement data. By using deep learning to set up a surrogate model for electromagnetic induction inverse problem, the vertical distribution of soil salinity during salt leaching at non-growth season is revealed. HYDRUS-1D was used to perform inversions of soil hydraulic parameters to obtain accurate numerical solutions. The results show that the surrogate of electromagnetic induction inversion using a deep convolutional encoding-decoding network (NET) with a learning rate of 0.0006 and 1500 sets of training samples has high accuracy, with an R2 of 0.9930 and RMSE of 0.2585 mg/cm3. The HYDRUS inverse modeling was performed using synthetic ECa data, water content and soil salinity observation data, and the combination of ECa and shallow ground (10 cm deep) water content respectively. It was shown that although the accuracy of the water-salt transport model obtained by using ECa data is slightly lower than that of the inversion model using traditional observation data, it can still accurately simulate the water-salt distribution and has the incomparable advantages of being fast, convenient and low-cost, and the accuracy of the model is improved to some extent by incorporating shallow ground water content data into the inversion based on ECa data. Therefore, the water-salt transport simulation framework incorporating soil apparent electrical conductivity proposed in this study can accurately simulate salt transport and can provide strategies and methods for the simulation and prediction of soil water-salt transport using electromagnetic induction technology.