Airborne gravimetry data sparse reconstruction via L1-norm convex quadratic programming
Yang Ya-Peng1, Wu Mei-Ping1, and Tang Gang2
1. College of Mechatronics and Automation, National University of Defense Technology, Changsha 410073, China.
2. College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
Abstract In practice, airborne gravimetry is a sub-Nyquist sampling method because of the restrictions imposed by national boundaries, financial cost, and database size. In this study, we analyze the sparsity of airborne gravimetry data by using the discrete Fourier transform and propose a reconstruction method based on the theory of compressed sensing for large-scale gravity anomaly data. Consequently, the reconstruction of the gravity anomaly data is transformed to a L1-norm convex quadratic programming problem. We combine the preconditioned conjugate gradient algorithm (PCG) and the improved interior-point method (IPM) to solve the convex quadratic programming problem. Furthermore, a flight test was carried out with the homegrown strapdown airborne gravimeter SGA-WZ. Subsequently, we reconstructed the gravity anomaly data of the flight test, and then, we compared the proposed method with the linear interpolation method, which is commonly used in airborne gravimetry. The test results show that the PCG–IPM algorithm can be used to reconstruct large-scale gravity anomaly data with higher accuracy and more effectiveness than the linear interpolation method.
The research is supported by the National High Technology Research and Development Program of China (No. SS2013AA060402).
Cite this article:
Yang Ya-Peng,Wu Mei-Ping,Tang Gang. Airborne gravimetry data sparse reconstruction via L1-norm convex quadratic programming[J]. APPLIED GEOPHYSICS, 2015, 12(2): 147-156.
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2015, Vol. 12 
