Audio-frequency magnetotelluric data inversion method based on adaptive binary structure constraint and its application in geothermal exploration

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Audio-frequency magnetotelluric (AMT) inversion technology plays a crucial role in the exploration of deep resources, especially in the detection of deep geothermal resources. Although AMT inversion technology has significant advantages in vertical resolution, the inversion process still faces serious challenges of non-uniqueness. To improve the accuracy and reliability of inversion, this paper proposes an AMT data inversion method based on adaptive binary structure constraint. This method incorporates adaptive extraction techniques to conduct a cluster analysis of the resistivity model generated in each iteration, thereby intelligently identifying and distinguishing the target and background areas in the model. These target areas are then used as prior structural information for the next iteration, guiding the refined reconstruction of the resistivity model. During the iteration process, the AMT inversion algorithm focuses on calculating the target areas, while the resistivity values of the background areas remain unchanged. This strategy effectively reduces the degrees of freedom in the inversion problem, lowers the dimension of the solution, thereby improving the computational efficiency and accuracy of the inversion. Model tests show that compared with traditional smoothing constrained AMT inversion method (S-AMT), the adaptive binary structure-constrained AMT inversion method (ABS-AMT) can reconstruct high-resolution refined resistivity models, significantly reducing the uncertainty of inversion. Furthermore, the ABS-AMT method not only reduces the dependence on prior knowledge but also enhances the algorithm's adaptability to different geological conditions. Practical application in the geothermal area of Laosandui in Linjiang City, Jilin Province, China, fully demonstrates the method's effectiveness and significant superiority in actual geothermal exploration through comparative analysis with known geothermal target areas.