The Study Of Resistivity Inversion Based On Bayesian-Sparsity Constraint Regularization Method

The inversion of resistivity is an important ill-posed problem in the inverse probl-em of mathematical physics.The main principle of resistivity imaging is that:accord-ing to different internal structures in the measured area and different conductivity,the injected current signal and date obtained through measuring the different are used to resistivity distribution image within the region.In this paper,we use the method of sparse constraint regularization and Bayesian inference to solve the problem of resistivity inversion.The introduction of Bayesian in-ference reduces the smoothness requirement of sparse constraint term and produces a set of sampling points of convergence to posterior.In this paper,the resistivity model of anomalous body with irregular shape and irregular shape with multiple corners is numerically simulated.We can get the expectation and the standard deviation of effect-ive samplings by samplings by using l1 constraint and TV constraint.We can obtain that Bayesian-sparsity constraint regularization method is feasible and effective when dealing with the resistivity model problem with abnormal structure with sharp and angular boundaries.In addition,for the second resistivity model,the anti-noise ability of Bayesian-sparsity constraint regularization method is verified by comparing the inversion image with noise and the image.