SUMMARY Large-scale modelling of 3-D controlled-source electromagnetic (CSEM) surveys used to be feasible only for large companies and research consortia. This has changed over the last few years, and today there exists a selection of different open-source codes available to everyone. Using four different codes in the Python ecosystem, we perform simulations for increasingly complex models in a shallow marine setting. We first verify the computed fields with semi-analytical solutions for a simple layered model. Then we validate the responses of a more complex block model by comparing results obtained from each code. Finally, we compare the responses of a real-world model with results from the industry. On the one hand, these validations show that the open-source codes are able to compute comparable CSEM responses for challenging, large-scale models. On the other hand, they show many general and method-dependent problems that need to be faced for obtaining accurate results. Our comparison includes finite-element and finite-volume codes using structured rectilinear and octree meshes as well as unstructured tetrahedral meshes. Accurate responses can be obtained independently of the chosen method and the chosen mesh type. The runtime and memory requirements vary greatly based on the choice of iterative or direct solvers. However, we have found that much more time was spent on designing the mesh and setting up the simulations than running the actual computation. The challenging task is, irrespective of the chosen code, to appropriately discretize the model. We provide three models, each with their corresponding discretization and responses of four codes, which can be used for validation of new and existing codes. The collaboration of four code maintainers trying to achieve the same task brought in the end all four codes a significant step further. This includes improved meshing and interpolation capabilities, resulting in shorter runtimes for the same accuracy. We hope that these results may be useful for the CSEM community at large and that we can build over time a suite of benchmarks that will help to increase the confidence in existing and new 3-D CSEM codes.

中文翻译：

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迈向 3-D CSEM 建模的开源环境

总结 3-D 受控源电磁 (CSEM) 调查的大规模建模过去仅适用于大公司和研究联盟。在过去的几年里，这种情况发生了变化，如今，每个人都可以选择不同的开源代码。我们使用 Python 生态系统中的四种不同代码，对浅海环境中日益复杂的模型进行模拟。我们首先使用简单分层模型的半解析解来验证计算场。然后，我们通过比较从每个代码中获得的结果来验证更复杂的块模型的响应。最后，我们将真实世界模型的响应与行业结果进行比较。一方面，这些验证表明，开源代码能够计算出可比较的 CSEM 响应以应对挑战，大型模型。另一方面，它们显示了获得准确结果需要面对的许多一般性和方法相关的问题。我们的比较包括使用结构化直线和八叉树网格以及非结构化四面体网格的有限元和有限体积代码。可以独立于所选方法和所选网格类型获得准确的响应。根据迭代或直接求解器的选择，运行时间和内存要求有很大差异。然而，我们发现设计网格和设置模拟所花费的时间比运行实际计算要多得多。无论选择何种代码，具有挑战性的任务是适当地离散化模型。我们提供了三个模型，每个模型都有相应的离散化和四个代码的响应，可用于验证新的和现有的代码。试图完成相同任务的四个代码维护者的协作最终使所有四个代码都更进一步。这包括改进的网格划分和插值功能，从而缩短了相同精度的运行时间。我们希望这些结果可能对整个 CSEM 社区有用，并且我们可以随着时间的推移建立一套基准，这将有助于提高对现有和新的 3-D CSEM 代码的信心。