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Fast modeling of gravity gradients from topographic surface data using GPU parallel algorithm
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作者 Xuli Tan Qingbin Wang +2 位作者 Jinkai Feng Yan Huang Ziyan Huang 《Geodesy and Geodynamics》 CSCD 2021年第4期288-297,共10页
The gravity gradient is a secondary derivative of gravity potential,containing more high-frequency information of Earth’s gravity field.Gravity gradient observation data require deducting its prior and intrinsic part... The gravity gradient is a secondary derivative of gravity potential,containing more high-frequency information 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 formulas 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 algorithm using 100 elevation data in two topographic areas(rough and moderate terrain).Modeling differences 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 computational efficiency approximately 200 times higher than the serial algorithm in experiments,demonstrating 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. 展开更多
关键词 Gravity gradient topographic surface data Rectangle prism method Parallel computation Graphical processing unit(GPU)
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A modified TOPMODEL introducing the bedrock surface topographic index in Huangbengliu watershed,China 被引量:1
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作者 QIU An-ni ZHANG Yan-jun +4 位作者 WANG Gen-xu CUI Jun-fang SONG Yuan-xin SUN Xiang-yang CHEN Li 《Journal of Mountain Science》 SCIE CSCD 2022年第12期3517-3532,共16页
Subsurface stormflow is a dominant runoff mechanism in steep humid mountainous areas.An insite measurement of subsurface stormflow suggests that the bedrock surface plays an important role in the runoff generation and... Subsurface stormflow is a dominant runoff mechanism in steep humid mountainous areas.An insite measurement of subsurface stormflow suggests that the bedrock surface plays an important role in the runoff generation and routing process,which was rarely adopted in hydrological modelling studies.To improve the runoff simulation performance,the bedrock surface topographic index is introduced,and a modified TOPMODEL based on the bedrock surface topographic index is developed to simulate the runoff.The modified TOPMODEL is applied to the Huangbengliu(HBL),a steep watershed in Gongga Mountain,and proved to be more appropriate for the HBL watershed,especially for peak simulation.The Nash-Sutcliffe model efficiency(NSE)is improved from 0.24 to 0.58 in the calibration period and from 0.40 to 0.62 in the verification period.The result of this study can advance the understanding of the mechanism of flash floods and contribute flood control and disaster prevention in the HBL watershed and similar areas. 展开更多
关键词 Subsurface stormflow Flash flood Modified TOPMODEL Bedrock surface topographic index Gongga Mountain
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