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

• Published in: Computers & geosciences Vol. 119; pp. 123 - 136
• Main Authors: Castillo-Reyes, Octavio, de la Puente, Josep, Cela, José María
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2018
• Subjects: computer software; computers; Edge finite element; electric field; equations; finite element analysis; geophysics; High-performance computing; isotropy; Marine electromagnetics; Numerical solutions; High-performance computing; Edge finite element; Numerical solutions; Marine electromagnetics
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2018.07.005

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 Nédélec 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. •Python language has potential for parallel computations in the geophysics field.•The electric field decomposition enhances the accuracy of the modeling.•Frequency-adapted mesh solutions are more efficient in terms of time and memory.•The parallel performance of the code is acceptable.•The algorithm might be useful as a kernel for inversions of EM datasets.