Three‐Dimensional Magnetotelluric Parallel Inversion Algorithm Using the Data‐Space Method

Up until now, the key issue in practical applications of three-dimensional magnetotelluric (3D MT) inversion is low efficiency of computation. By further analysis of the data-space inversion approach of 3D MT, we develop a massively parallel inversion scheme on the basis of frequency division and matrix decomposition, and implement its procedure by using MPI on the Dawn TC5000A high-performance computing platform. The algorithm we develop includes the parallel calculation of 3D forward modeling, sensitivity matrix and cross-product matrix, as well as the update of model parameters. The algorithm has the advantages of higher efficiency in computation and lower memory storage in which the storage amount of sensitivity matrix at every single computing node is 2/N times as much amount as a PC (N is the number of nodes included in parallel computation). Furthermore, we test the implemented scheme with synthetic data from two 3D theoretical models and analyze the computational efficiency under multiple-nodes computing. The numerical experiment results show that the 3D data-space parallel inversion algorithm is feasible and efficient. Compared with the implementation on single PC, the parallel scheme is not only able to improve the computing speed and shorten the computation time, but also enlarge the calculational scale, which would advance greatly the utility of 3D MT inversion.