Fast modeling of gravity gradients from topographic surface data using GPU parallel algorithm

• 作者: Xuli Tan Qingbin Wang Jinkai Feng Yan Huang Ziyan Huang
• 来源期刊: 大地测量与地球动力学（英文版）
• 年，卷（期）: 2021,（4）
• 页码范围: 288-297
• 页数: 10页
• 语种: 英文

摘要

The gravity gradient is a secondary derivative of gravity potential,containing more high-frequency in-formation of Earth's gravity field.Gravity gradient observation data require deducting its prior and intrinsic parts to obtain more variational information.A model generated from a topographic surface database is more appropriate to represent gradiometric effects derived from near-surface mass,as other kinds of data can hardly reach the spatial resolution requirement.The rectangle prism method,namely an analytic integration of Newtonian potential integrals,is a reliable and commonly used approach to modeling gravity gradient,whereas its computing efficiency is extremely low.A modified rectangle prism method and a graphical processing unit (GPU) parallel algorithm were proposed to speed up the modeling process.The modified method avoided massive redundant computations by deforming for-mulas according to the symmetries of prisms' integral regions,and the proposed algorithm parallelized this method's computing process.The parallel algorithm was compared with a conventional serial al-gorithm using 1" elevation data in two topographic areas (rough and moderate terrain).Modeling dif-ferences between the two algorithms were less than 0.1 E,which is attributed to precision differences between single-precision and double-precision float numbers.The parallel algorithm showed compu-tational efficiency approximately 200 times higher than the serial algorithm in experiments,demon-strating its effective speeding up in the modeling process.Further analysis indicates that both the modified method and computational parallelism through GPU contributed to the proposed algorithm's performances in experiments.