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A Review of High-Performance Computational Strategies for Modeling and Imaging of Electromagnetic Induction Data

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Abstract

Many geoscientific applications exploit electrostatic and electromagnetic fields to interrogate and map subsurface electrical resistivity—an important geophysical attribute for characterizing mineral, energy, and water resources. In complex three-dimensional geologies, where many of these resources remain to be found, resistivity mapping requires large-scale modeling and imaging capabilities, as well as the ability to treat significant data volumes, which can easily overwhelm single-core and modest multicore computing hardware. To treat such problems requires large-scale parallel computational resources, necessary for reducing the time to solution to a time frame acceptable to the exploration process. The recognition that significant parallel computing processes must be brought to bear on these problems gives rise to choices that must be made in parallel computing hardware and software. In this review, some of these choices are presented, along with the resulting trade-offs. We also discuss future trends in high-performance computing and the anticipated impact on electromagnetic (EM) geophysics. Topics discussed in this review article include a survey of parallel computing platforms, graphics processing units to multicore CPUs with a fast interconnect, along with effective parallel solvers and associated solver libraries effective for inductive EM modeling and imaging.

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Notes

  1. I consider the beginning of the HPC era circa 1988, with the development of multiple instructions multiple data (MIMD) asynchronous computing architectures (cf. Fox 1988).

  2. One petaflop is equivalent to 1015 operations per second.

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Acknowledgments

I wish to thank Yasuo Ogawa, Graham Heinson, and other members of the 21st EM Workshop Program Committee for the invitation and opportunity to write this review article. Input from the two referees, Klaus Spitzer and Chester Weiss, also improved the content of the review. Finally, I also wish to acknowledge my employer, Lawrence Berkeley Laboratory, and the U.S. Department of Energy Office of Science for funding, under contract number DE-AC02-05CH11231.

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    Gregory A. Newman

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Newman, G.A. A Review of High-Performance Computational Strategies for Modeling and Imaging of Electromagnetic Induction Data. Surv Geophys 35, 85–100 (2014). https://doi.org/10.1007/s10712-013-9260-0

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