Three-dimensional inversion algorithm of magnetotelluric data based on solution space dimensionality reduction technique

The magnetotelluric (MT) method is highly advantageous in geothermal resource exploration because of its deep probing capability, low cost, and sensitivity to key geothermal system elements such as heat sources, reservoirs, and caprocks. To address the challenge of balancing the MT inversion resolution and computational efficiency, we propose a Gauss–Newton optimization inversion algorithm based on the solution space dimensionality reduction technique. This algorithm uses solution space dimensionality reduction to establish algebraic mapping relationships between the edges of grid cells, effectively reducing the degrees of freedom in the linear equation system required for forward and pseudo-forward modeling, thereby saving computational costs. In addition, during the inversion process, a multiple right-hand direct–iterative hybrid solver is employed to solve the dimensionally reduced linear equation s, further enhancing the computational efficiency of the inversion. We then validate and analyze the accuracy and efficiency of the forward and inversion algorithms using various synthetic models. Finally, we apply this inversion strategy to field MT data from the Dabie Mountain geothermal area in eastern China. On the basis of the inversion results, we then analyze the thermal genesis model of the region, providing technical support for the exploration and development of deep geothermal resources.