GICUDA: A parallel program for 3D correlation imaging of large scale gravity and gravity gradiometry data on graphics processing units with CUDA

  • Authors:

  • Zhaoxi Chen;Xiaohong Meng;Lianghui Guo;Guofeng Liu

  • Affiliations:

  • State Key laboratory of Geological Process and Mineral Resources, Beijing 100083,China and School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083,China;State Key laboratory of Geological Process and Mineral Resources, Beijing 100083,China and School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083,China;State Key laboratory of Geological Process and Mineral Resources, Beijing 100083,China and School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083,China;State Key laboratory of Geological Process and Mineral Resources, Beijing 100083,China and School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083,China

  • Venue:
  • Computers & Geosciences
  • Year:
  • 2012

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Abstract

The 3D correlation imaging for gravity and gravity gradiometry data provides a rapid approach to the equivalent estimation of objective bodies with different density contrasts in the subsurface. The subsurface is divided into a 3D regular grid, and then a cross correlation between the observed data and the theoretical gravity anomaly due to a point mass source is calculated at each grid node. The resultant correlation coefficients are adopted to describe the equivalent mass distribution in a quantitate probability sense. However, when the size of the survey data is large, it is still computationally expensive. With the advent of the CUDA, GPUs lead to a new path for parallel computing, which have been widely applied in seismic processing, astronomy, molecular dynamics simulation, fluid mechanics and some other fields. We transfer the main time-consuming program of 3D correlation imaging into GPU device, where the program can be executed in a parallel way. The synthetic and real tests have been performed to validate the correctness of our code on NVIDIA GTX 550. The precision evaluation and performance speedup comparison of the CPU and GPU implementations are illustrated with different sizes of gravity data. When the size of grid nodes and observed data sets is 1024x1024x1 and 1024x1024, the speed up can reach to 81.5 for gravity data and 90.7 for gravity vertical gradient data respectively, thus providing the basis for the rapid interpretation of gravity and gravity gradiometry data.