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）

发布时间：2018-03-12 20:00

本文选题：全张量重力梯度(FTG)　切入点：ICCG方法　出处：《Applied Geophysics》2017年02期 　论文类型：期刊论文

【摘要】：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。
[Abstract]:With the development of gravity gradient gradients (FTG) measurement technology of all Zhang Liang, 3D inversion of gravity gradient data has been paid more and more attention in oil, gas and mineral exploration. The application of GPU(Graphics Processing unit and preconditioning methods in geophysical inversion is very important. In this paper, the improvement of the conjugate gradient algorithm of incomplete Giolesky decomposition and symmetric successive overrelaxation SSOR is presented. Preconditioned conjugate gradient method, And considering that method preprocessing takes extra time, The GPU parallel algorithm of the algorithm is developed to improve the acceleration effect. Then, the adaptability of the improved parallel preprocessing method to the 3D full Zhang Liang gravity gradient data inversion is proved by the noisy model data inversion. The parallel SSOR-ICCG algorithm based on NVIDIATesla C2050 GPU is compared with the serial program at 2.0 GHz CPU, and the speedup ratio is about 25 times. Finally, we apply the algorithm to the inversion of airborne gravimetric gradient data measured in Vinton Salt Dome in southern Louisiana, USA. The inversion results show that this method has the advantages and feasibility of fast inversion of 3D full Zhang Liang gravity gradient data.
【作者单位】：吉林大学地球探测科学与技术学院;天津航海仪器研究所;吉林省电力勘测设计院;
【基金】：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) the National Basic Research Program of China(973 Program)(No.2015CB45300)
【分类号】：P631.1

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