3D resistivity inversion with multiple priori-information constraint
Guo Laigong1,2, Dai Guanglong2, Yang Bencai2, Xue Junhua3, Chen Benliang3
1. School of Electronics and Information Engineering, Anhui University of Sciences and Technology, Huainan, Anhui 232001, China; 2. School of Mining and Safety Engineering, Anhui University of Sciences and Technology, Huainan, Anhui 232001, China; 3. State Key Laboratory of Deep Coal Mining & Environment Protection, Huainan, Anhui 232001, China
Abstract:The use of prior-information constraint can improve the inversion accuracy of 3D DC electrical resistivity.However,in most cases,single prior-information is used to modify the objective function and the results are inaccurate.In order to solving the problems,the inversion equation of iterative re-weighted least-squares is modified; parameters such as the structural metric,weight mean,and standard deviation are added; and the weight function of the model parameters is replaced by the error function and complementary error function.Then different prior-information can constrain the inversion with modifying parameters of weight function in different zones.The parallel conjugate gradient least-square method is used to effectively improve the inversion calculation.In synthetic data inversions,smoothness constraint,block constraint,and resistivity extreme-constraint are used.The results show that the inversion constrained with multiple priori-information is more accurate than that with single prior-information.In practice,parameters of weight functions can be selected based on geological structures to improve the accuracy of 3D resistivity inversion and the calculation efficiency.
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