Parallel 3-D marine controlled-source electromagnetic modelling using high-order tetrahedral Nédélec elements. Issue 1 (24th June 2019)

• Record Type: Journal Article
• Authors: Castillo-Reyes, Octavio; de la Puente, Josep; García-Castillo, Luis Emilio; Cela, José María
• Is Part Of: Geophysical journal international. Volume 219:Issue 1(2019)
• Page Start: 39
• Page End: 65
• Publication Date: 2019-06-24
• Subjects: Controlled source electromagnetics (CSEM) -- Numerical modelling -- Numerical solutions; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggz285
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: We present a parallel and high-order Nédélec finite element solution for the marine controlled-source electromagnetic (CSEM) forward problem in 3-D media with isotropic conductivity. Our parallel Python code is implemented on unstructured tetrahedral meshes, which support multiple-scale structures and bathymetry for general marine 3-D CSEM modelling applications. Based on a primary/secondary field approach, we solve the diffusive form of Maxwell's equations in the low-frequency domain. We investigate the accuracy and performance advantages of our new high-order algorithm against a low-order implementation proposed in our previous work. The numerical precision of our high-order method has been successfully verified by comparisons against previously published results that are relevant in terms of scale and geological properties. A convergence study confirms that high-order polynomials offer a better trade-off between accuracy and computation time. However, the optimum choice of the polynomial order depends on both the input model and the required accuracy as revealed by our tests. Also, we extend our adaptive-meshing strategy to high-order tetrahedral elements. Using adapted meshes to both physical parameters and high-order schemes, we are able to achieve a significant reduction in computational cost without sacrificing accuracy in the modelling. Furthermore, we demonstrate the excellent performance and quasi-linear scaling of our implementation in a state-of-the-art high-performance computing architecture.