Tailored meshing for parallel 3D electromagnetic modeling using high-order edge elements

TL;DR

This paper investigates high-order edge element methods with adaptive mesh refinement for 3D electromagnetic modeling in geophysics, demonstrating their efficiency and accuracy on parallel computers with various refinement strategies.

Contribution

It extends the PETGEM code with supervised $h+p$ refinement strategies and analyzes their performance for realistic 3D EM modeling scenarios.

Findings

$h$-refinement efficiently achieves target accuracy.

$p=2$ offers optimal accuracy/performance balance.

Hierarchical $p$-refinement improves computational efficiency.

Abstract

We present numerical experiments for geophysics electromagnetic (EM) modeling based upon high-order edge elements and supervised $h+p$ refinement approaches on massively parallel computers. Our high-order $h+p$ refinement strategy is based on and extends the PETGEM code. We focus on the performance study in terms of accuracy, convergence rate, and computational effort to solve real-life 3D setups based on synthetic and experimental data for energy reservoir characterization. These test cases show variable resolution discretization needs and realistic physical parameters. In general, our numerical results are consistent theoretically. The use of $h-$adapted meshes was efficient to achieve a certain accuracy level in the synthetic EM responses. Regarding global $p-$refinement, $p=2$ exhibits the best accuracy/performance trade-off. Selective $p$-refinement might offer a better compromise between accuracy and computational cost. However, for $p-$refinement at different entities, the best refinement scheme consists of using $p=3$ at the volume level with $p=1$ at faces and edges. Thus, $p-$refinement can be competitive if applied hierarchically. Nevertheless, we acknowledge that the performance of our supervised $h+p$ refinement strategy depends on the input model (e.g., conductivity, frequency, domain decomposition strategy, among others). Whatever the chosen configuration, our numerical results provide an in-depth understanding of EM modeling's pros and cons when supervised $h+p$ refinement schemes are applied.