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

改进的预处理共轭梯度快速算法在三维重力梯度数据反演中的应用（英文）

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 noise- contaminated 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 airbome gravity-gradiometry data from Vinton salt dome （south- west 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.

随着全张量重力梯度(FTG)测量技术的不断发展,重力梯度数据的三维反演技术在油气和矿产勘探中日益受到重视与关注。为了快速处理和解释大规模的高精度数据,图形处理器GPU(Graphics Processing Unit)和预处理分解技术(Preconditioning methods)在地球物理反演中的使用变得十分重要。本文结合对称逐次超松弛(SSOR)技术与不完全乔列斯基分解共轭梯度算法(ICCG)提出改进的预处理共轭梯度法,并考虑到方法预处理分解占用额外的时间,开发该算法的GPU并行算法来提高加速效果。然后通过含噪的模型数据反演来证明改进的并行预处理方法在三维全张量重力梯度数据反演中的适应性。由此,基于NVIDIATesla C2050 GPU的并行SSOR-ICCG算法和在2.0GHz CPU上的串行程序比较,达到了大约25倍的加速比。最后,我们将该算法应用于美国路易斯安那州南方Vinton盐丘的实测航空重力梯度数据反演中,反演出良好的反演结果,验证了该方法在三维全张量重力梯度数据快速反演中的优势和可行性。