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

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.

Bathymetric Prediction from Multi-source Satellite Altimetry Gravity Data

According to the "theoretical admittance " and the "observation admittance" of the actual data,the theoretical value of effective elastic thickness in the study area was 10 km.Combining the gravity anomalies and vertical gravity gradient anomalies,the admittance function is used to construct the 1′×1′ bathymetry model over the Philippine Sea by using the adaptive weighting technique.It is found that the accuracy of the bathymetry model constructed is the highest when the ratio of inversion result of vertical gravity gradient anomalies and inversion result of gravity anomalies is 2∶3.At the same time,using multi-source gravity data to predict bathymetry could synthesize the superiority of gravity anomalies and vertical gravity gradient anomalies on the different seafloor topography,and the accuracy is better than bathymetry model that only used gravity anomalies or vertical gravity gradient anomalies.Taking the ship test data as the checking condition,the accuracy of predicting model is slightly lower than that of V18.1 model and improved by 27.17% and 39.02% respectively compared with the ETOPO1 model and the DTU10 model.Check points which the absolute value of the relative error of the predicting model is in the range of 5% accounted for 94.25% of the total.