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Improved preconditioned conjugate gradient algorithm and application in 3D inversion of gravity-gradiometry data

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Abstract

With the continuous development of full tensor gradiometer (FTG) measurement techniques, three-dimensional (3D) inversion of FTG data is becoming increasingly used in oil and gas exploration. In the fast processing and interpretation of large-scale high-precision data, the use of the graphics processing unit process unit (GPU) and preconditioning methods are very important in the data inversion. In this paper, an improved preconditioned conjugate gradient algorithm is proposed by combining the symmetric successive over-relaxation (SSOR) technique and the incomplete Choleksy decomposition conjugate gradient algorithm (ICCG). Since preparing the preconditioner requires extra time, a parallel implement based on GPU is proposed. The improved method is then applied in the inversion of noisecontaminated synthetic data to prove its adaptability in the inversion of 3D FTG data. Results show that the parallel SSOR-ICCG algorithm based on NVIDIA Tesla C2050 GPU achieves a speedup of approximately 25 times that of a serial program using a 2.0 GHz Central Processing Unit (CPU). Real airborne gravity-gradiometry data from Vinton salt dome (southwest Louisiana, USA) are also considered. Good results are obtained, which verifies the efficiency and feasibility of the proposed parallel method in fast inversion of 3D FTG data.

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Acknowledgments

The authors would like to thank Bell Geospace Inc. for providing FTG data from the Vinton salt dome. We also thank the reviewers for their detailed comments and suggestions, which helped to improve the paper.

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Authors and Affiliations

  1. College of Geo-Exploration Science and Technology, Jilin University, Changchun, 130021, China

    Tai-Han Wang, Da-Nian Huang & Guo-Qing Ma

  2. Tianjin Navigation Instrument Research Institute, Tianjin, 300131, China

    Zhao-Hai Meng

  3. Jilin Provincial Electric Power Survey and Design Institute, Changchun, 130021, China

    Ye Li

Authors
  1. Tai-Han Wang
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  2. Da-Nian Huang
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  3. Guo-Qing Ma
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  4. Zhao-Hai Meng
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  5. Ye Li
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Corresponding author

Correspondence to Tai-Han Wang.

Additional information

This research was supported by the Sub-project of National Science and Technology Major Project of China (No. 2016ZX05027-002-003), the National Natural Science Foundation of China (No. 41404089), the State Key Program of National Natural Science of China (No. 41430322) and the National Basic Research Program of China (973 Program) (No. 2015CB45300).

Wang Tai-Han received his B.S. (2013) in Geophysics at the College of Geo-Exploration Science and Technology, Jilin University, and is currently a Ph.D. candidate in Solid Geophysics at the college. His major research interests are in the field of processing and fast inversion of gravity, magnetic, and gradient tensor data.

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Wang, TH., Huang, DN., Ma, GQ. et al. Improved preconditioned conjugate gradient algorithm and application in 3D inversion of gravity-gradiometry data. Appl. Geophys. 14, 301–313 (2017). https://doi.org/10.1007/s11770-017-0625-x

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  • DOI: https://doi.org/10.1007/s11770-017-0625-x

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