3-D Adaptive Multinary Inversion of Magnetotelluric Data Using Unstructured Tetrahedral Mesh

Abstract

The resistivity distribution obtained from traditional inversion methods for magnetotelluric (MT) data often lacks clarity, making it difficult to delineate boundaries between host media and anomalous targets. To address this, we developed a novel 3-D MT inversion approach based on the multinary transformation of model parameters. This method transforms the model resistivity distribution into a desired step-function-like form, enabling explicit identification of interfaces between geological units. The sharpness of the recovered resistivity model is controlled by the standard deviation of the multinary transformation’s error function, and an adaptive technique is introduced to adjust this parameter during the inversion process to account for deviations between true and discrete values in the multinary space. The inversion problem is solved using a data-space Gauss-Newton approach, which enhances memory efficiency and convergence speed. Additionally, unstructured tetrahedral meshes are utilized to accurately model rugged topography and complex geoelectric structures. Synthetic model studies demonstrate the superiority of the adaptive multinary inversion over conventional maximum smoothness inversion and fixed standard deviation multinary inversion. Finally, the method is applied to image subsurface resistivity in the northwest Geysers geothermal field in California, USA, showcasing its effectiveness.