PETGEM: A parallel code for 3D CSEM forward modeling using edge finite elements

TL;DR

PETGEM is an open-source, parallel Python software that efficiently models 3D marine electromagnetic problems using edge finite elements, supporting unstructured meshes and demonstrating high accuracy and scalability.

Contribution

This paper introduces PETGEM, a novel parallel Python code employing Nédélec edge finite elements for accurate 3D CSEM modeling with unstructured meshes and adaptive meshing capabilities.

Findings

PETGEM achieves high accuracy in classical and real-world tests.

The code supports unstructured tetrahedral meshes.

Scalability is demonstrated on HPC architectures.

Abstract

We present the capabilities and results of the Parallel Edge-based Tool for Geophysical Electromagnetic modeling (PETGEM), as well as the physical and numerical foundations upon which it has been developed. PETGEM is an open-source and distributed parallel Python code for fast and highly accurate modeling of 3D marine controlled-source electromagnetic (3D CSEM) problems. We employ the N\'ed\'elec Edge Finite Element Method (EFEM) which offers a good trade-off between accuracy and number of degrees of freedom, while naturally supporting unstructured tetrahedral meshes. We have particularised this new modeling tool to the 3D CSEM problem for infinitesimal point dipoles asumming arbitrarily isotropic media for low-frequencies approximations. In order to avoid source-singularities, PETGEM solves the frequency-domain Maxwell's equations of the secondary electric field, and the primary electric field is calculated analytically for homogeneous background media. We assess the PETGEM accuracy using classical tests with known analytical solutions as well as recent published data of real life geological scenarios. This assessment proves that this new modeling tool reproduces expected accurate solutions in the former tests, and its flexibility on realistic 3D electromagnetic problems. Furthermore, an automatic mesh adaptation strategy for a given frequency and specific source position is presented. We also include a scalability study based on fundamental metrics for high-performance computing (HPC) architectures.