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3D density inversion of gravity gradiometry data with a multilevel hybrid parallel algorithm

  • Gravity and Magnetic Exploration Methods
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Abstract

The density inversion of gravity gradiometry data has attracted considerable attention; however, in large datasets, the multiplicity and low depth resolution as well as efficiency are constrained by time and computer memory requirements. To solve these problems, we improve the reweighting focusing inversion and probability tomography inversion with joint multiple tensors and prior information constraints, and assess the inversion results, computing efficiency, and dataset size. A Message Passing Interface (MPI)-Open Multi-Processing (OpenMP)-Computed Unified Device Architecture (CUDA) multilevel hybrid parallel inversion, named Hybrinv for short, is proposed. Using model and real data from the Vinton Dome, we confirm that Hybrinv can be used to compute the density distribution. For data size of 100×100×20, the hybrid parallel algorithm is fast and based on the run time and scalability we infer that it can be used to process the large-scale data.

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Acknowledgments

We thank Bell Geospace Inc. for permission to use the FTG data from the Vinton Dome. The authors also are very grateful to the anonymous reviewers for the helpful comments comments and valuable suggestions which improved this manuscript significantly

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Authors and Affiliations

  1. Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, School of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China

    Zhen-Long Hou, En-De Wang & Hao Cheng

  2. College of Geo-exploration Science and Technology, Jilin University, Changchun, 130026, China

    Da-Nian Huang

Authors
  1. Zhen-Long Hou
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  2. Da-Nian Huang
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  3. En-De Wang
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  4. Hao Cheng
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Corresponding author

Correspondence to Zhen-Long Hou.

Additional information

Financial support by the China Postdoctoral Science Foundation (2017M621151), Northeastern University Postdoctoral Science Foundation (20180313), the Fundamental Research Funds for Central Universities (N180104020) and NSFC-Shandong Joint Fund of the National Natural Science Foundation of China (U1806208).

Hou Zhen-Long received his B.S. in Geophysics from Jilin University in 2011 and his Ph.D. in Computer Systems and Architecture from Jilin University in 2016. He is currently a postdoctoral fellow in Northeastern University. His main research interests are gravity & magnetic exploration data processing & interpretation, and parallel computing methods.

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Hou, ZL., Huang, DN., Wang, ED. et al. 3D density inversion of gravity gradiometry data with a multilevel hybrid parallel algorithm. Appl. Geophys. 16, 141–152 (2019). https://doi.org/10.1007/s11770-019-0763-4

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  • DOI: https://doi.org/10.1007/s11770-019-0763-4

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