Utilizing massively parallel co-processors in the AarhusInv 1D forward and inverse AEM modelling code

Casper Kirkegaard; Kristoffer Andersen; Tue Boesen; Anders V. Christiansen; Esben Auken; Gianluca Fiandaca

doi:10.1071/ASEG2015ab125

24th International Geophysical Conference and Exhibition – Geophysics and Geology Together for Discovery

ISSN: 2202-0586
E-ISSN:

oa Utilizing massively parallel co-processors in the AarhusInv 1D forward and inverse AEM modelling code
Authors Casper Kirkegaard^a, Kristoffer Andersen^a, Tue Boesen^a, Anders V. Christiansen^a, Esben Auken^a and Gianluca Fiandaca^a
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^a Aarhus University, C.F. Moellers Allé 4 8000, Aarhus C, - DK [email protected]
Publisher: Australian Society of Exploration Geophysicists
Source: ASEG Extended Abstracts, Volume 2015, Issue 24th International Geophysical Conference and Exhibition – Geophysics and Geology Together for Discovery, Dec 2015, p. 1 - 3
DOI: https://doi.org/10.1071/ASEG2015ab125
- Published online: 01 Dec 2015

Abstract

While the forefront of AEM research is focusing on the challenges of 3D modelling, the wide AEM community still rely on less sophisticated computational techniques for their calculations. Inversion of large time domain AEM surveys still prove a computational challenge within a 1D formulation, and require much more computational resources than can be delivered by an office workstation. Emerging Monte-Carlo based 1D Bayesian inversion schemes provide another example of applications that are currently limited by the 1D forward modelling rate.

In this abstract we describe our research in modifying the AarhusInv AEM inversion code to utilize next generation massively parallel co-processors. While our results are early and based on very little optimization, we still achieve comparable levels of performance (>80%) from a single co-processor and a 48 cpu core server. We estimate that performance on the co-processor can be speeded up by approximately another 4x with a limited amount of code restructuring/rewriting.

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References

Auken E., Christiansen A.V., Kirkegaard C., Fiandaca G., Schamper C., Behroozmand A.A., Binley A., Nielsen E., Efferse F., Christensen N.B., Serensen K.I., Foged N., and Vignoli G. 2014. An overview of a highly versatile forward and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric data. Exploration Geophysics 1-13, doi: 10.1071/EG13097.
Brodie R. and Sambridge M. 2006. A holistic approach to inversion of frequency-domain airborne EM data. Geophysics 71, G301-G312.
Johansen H.K. and Serensen K.I. 1979. Fast Hankel transforms. Geophysical Prospecting 27, 876-901.
Kirkegaard C. and Auken E. 2014. A parallel, scalable and memory efficient inversion code for very large scale airborne EM surveys. Geophysical Prospecting, Accepted.
Minsley B.J. 2011. A trans-dimensional Bayesian Markov chain Monte Carlo algorithm for model assessment using frequency-domain electromagnetic data. Geophysical Journal International 187,252-272
Ward S.H. and Hohmann G.W. 1988. Electromagnetic theory for geophysical applications. In: Electromagnetic methods in applied geophysics, Vol. 1 (ed. M.N. Nabighian), pp. 131-311. Society of exploration geophysicists (SEG).

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Article Type: Research Article

Keyword(s): AEM; forward modelling; High performance computing; inversion

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oa Utilizing massively parallel co-processors in the AarhusInv 1D forward and inverse AEM modelling code

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