Name
Title | ||
|---|---|---|
An Efficient Preconditioner for 3-D Finite Difference Modeling of the Electromagnetic Diffusion Process in the Frequency Domain |
Abstract | ||
|---|---|---|
Krylov subspace solvers for frequency-domain electromagnetic forward modeling problems converge remarkably more slowly as the period increases. In this article, we present an efficient four-color cellblock Gauss Seidel (GS) preconditioner for finite-difference (FD) electromagnetic modeling in geophysical applications. Rather than updating the FD electromagnetic (EM) equation edge by edge, as in a traditional GS scheme, we renew six edge components attached to one node simultaneously (i.e., in cellblock manner) effectively enforcing a local divergence free condition for currents. To improve implementation efficiency, we reorder the nodes on the FD grid into four colors so that nodes in each color are uncoupled, allowing the use of highly parallel vectorized algorithms. The four-color cellblock GS preconditioner is implemented in the MATLAB code, in conjunction with a BiCGstab solver. It is compared, in terms of iteration number and computing time, with other three commonly used preconditioners [GS, symmetric successive overrelaxation (SSOR) and incomplete lower and upper triangular matrix decomposition (ILU)] on three models—two synthetic and one modified from the version of real data inversion. The comparison indicates that the proposed algorithm is extremely stable and efficient compared with the other three preconditioners tested, over a range of periods (1-1000 s). Especially at long periods, the improvement of our proposed algorithm is substantial. In addition, a parallel implementation of the cellblock GS preconditioner is straightforward due to the independence of nodes in each color. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TGRS.2019.2937742 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Mathematical model,Color,Electromagnetics,Conductivity,Solid modeling,Matrix decomposition,Boundary conditions | Frequency domain,Diffusion process,Computer vision,Preconditioner,Finite difference,Computational science,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
58 | 1 | 0196-2892 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Li | 1 | 1 | 1.30 |
| Jianxin Liu | 2 | 0 | 3.38 |
| Gary D. Egbert | 3 | 10 | 2.80 |
| Rong Liu | 4 | 0 | 0.68 |
| Rongwen Guo | 5 | 1 | 1.04 |
| kejia pan | 6 | 22 | 5.73 |