Method for identifying parameters of non-Gaussian aquifer by fusing underground water level and natural potential data based on convolutional neural network

Abstract

The invention discloses a method for identifying non-Gaussian aquifer parameters by fusing underground water level and natural potential data based on a convolutional neural network, which comprises the following steps: 1, training a CVAE network by using a non-Gaussian aquifer parameter field sample, sampling from standard normal distribution, and initializing an estimation set of a latent vector z; 2, inputting the latent vector z estimation set into a trained CVAE decoder, and reconstructing a corresponding non-Gaussian aquifer parameter field estimation set; 3, during a harmonic water pumping test, based on the reconstructed non-Gaussian aquifer parameter field estimation set, operating a hydrological geophysical forward modeling model to obtain hydraulic water head and natural potential simulation data; 4, iteratively updating the latent vector z estimation set by adopting an ESMDA method in combination with the hydraulic head and natural potential observation data; repeating the steps 2 to 4 until the maximum number of iterations is reached; and 5, for the posterior set of the latent vector z obtained by updating, reconstructing through a CVAE decoder to obtain an estimation result of a non-Gaussian aquifer parameter field.

Description

technical field [0001] The invention relates to the technical field of groundwater numerical simulation, in particular to a method for identifying parameters of non-Gaussian aquifers based on convolutional neural network fusion of groundwater level and natural potential data. Background technique [0002] Numerical simulation methods have been widely used in groundwater management and remediation of contaminated aquifers. Reliable groundwater models require accurate knowledge of subsurface aquifer properties, such as hydraulic conductivity. However, limited by the number and location of boreholes, it is usually difficult to obtain comprehensive aquifer information by directly measuring aquifer-related parameters through boreholes. Therefore, the current main approach is to characterize the aquifer structure through stochastic inversion methods based on indirect observation data (such as water head and solute concentration). [0003] The ensemble-based data assimilation met...

AI Technical Summary

Problems solved by technology

[0005] Purpose of the invention: In order to overcome the problems of insufficient borehole observation data and limited estimation accuracy in the traditional non-Gaussian permeability coefficient field inversion, the present invention proposes a method based on convolutional neural network fusion of groundwater level and spontaneous potential data to identify non-Gaussian water content By introducing the natural potential method in the geophysical method and integrating the low-cost natural potential data, under the premise of limited hydrogeological observation data, the self-potential data is used as a supplement to realize the fine precision of the non-Gaussian permeability coefficient field. identify

Images