We present an elaborate preconditioning scheme for Krylov subspace methods which has been developed to improve the performance and reduce the execution time of parallel node-based finite-element solvers for three-dimensional electromagnetic numerical modelling in exploration geophysics. This new preconditioner is based on algebraic multigrid that uses different basic relaxation methods, such as Jacobi, symmetric successive over-relaxation and Gauss-Seidel, as smoothers and the wave-front algorithm to create groups, which are used for a coarse-level generation. We have implemented and tested this new preconditioner within our parallel nodal finite-element solver for three-dimensional forward problems in electromagnetic induction geophysics. We have performed series of experiments for several models with different conductivity structures and characteristics to test the performance of our algebraic multigrid preconditioning technique when combined with biconjugate gradient stabilised method. The results have shown that, the more challenging the problem is in terms of conductivity contrasts, ratio between the sizes of grid elements and/or frequency, the more benefit is obtained by using this preconditioner. Compared to other preconditioning schemes, such as diagonal, symmetric successive over-relaxation and truncated approximate inverse, the algebraic multigrid preconditioner greatly improves the convergence of the iterative solver for all tested models. Also, when it comes to cases in which other preconditioners succeed to converge to a desired precision, algebraic multigrid is able to considerably reduce the total execution time of the forward-problem code -up to an order of magnitude. Furthermore, the tests have confirmed that our algebraic multigrid scheme ensures grid-independent rate of convergence, as well as improvement in convergence regardless of how big local mesh refinements are. In addition, algebraic multigrid is designed to be a black-box preconditioner, which makes it easy to use and combine with different iterative methods. Finally, it has proved to be very practical and eficient in the parallel context.

Peer Reviewed