We implement a parallel algorithm with the advantage of MPI （Message Passing Interface） to speed up the rapid relaxation inversion for 3D magnetotelluric data. We test the parallel rapid relaxation algorithm with synthetic and real data. The execution efficiency of the algorithm for several different situations is also compared. The results indicate that the parallel rapid relaxation algorithm for 3D magnetotelluric inversion is effective. This parallel algorithm implemented on a common PC promotes the practical application of 3D magnetotelluric inversion and can be suitable for the other geophysical 3D modeling and inversion.
Currently, most of MT （magnetotelluric） data are still collected on sparse survey lines and interpreted using 2D inversion methods because of the field work cost, the work area environment, and so on. However, there are some 2D interpretation limitations of the MT data from 3D geoelectrical structures which always leads to wrong geological interpretations. In this paper, we used the 3D inversion method to interpret the MT sparse lines data. In model testing, the sparse lines data are the MT full information data generated from a test model and processed using the 3D conjugate gradients inversion code. The inversion results show that this inversion method is reasonable and effective. Meanwhile, we prove that for inversion results with different element parameters, the results by joint inversion of both the impedance tensor data and the tipper data are more accurate and closer to the test model.
Based on the analysis of the conjugate gradient algorithm, we implement a threedimensional （3D） conjugate gradient inversion algorithm with magnetotelluric impedance data. During the inversion process, the 3D conjugate gradient inversion algorithm doesn＇ t need to compute and store the Jacobian matrix but directly updates the model from the computation of the Jacobian matrix. Requiring only one forward and four pseudo-forward modeling applications per frequency to produce the model update at each iteration, this algorithm efficiently reduces the computation of the inversion. From a trial inversion with synthetic magnetotelluric data, the validity and stability of the 3D conjugate gradient inversion algorithm is verified.
