Experimental investigations on airborne gravimetry based on compressed sensing

Gravity surveys are an important research topic in geophysics and geodynamics. This paper investigates a method for high accuracy large scale gravity anomaly data reconstruction. Based on the airborne gravimetry technology, a flight test was carried out in China with the strap-down airborne gravimet...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2014-03, Vol.14 (3), p.5426-5440
Main Authors: Yang, Yapeng, Wu, Meiping, Wang, Jinling, Zhang, Kaidong, Cao, Juliang, Cai, Shaokun
Format: Article
Language:English
Subjects:
Accuracy
airborne gravimetry
Airborne sensing
Aircraft
Algorithms
Compressed
compressed sensing
Detection
Fourier transforms
Gravimetry
Gravity
Gravity anomalies
gravity anomaly data reconstruction
Inverse problems
Noise levels
orthogonal matching pursuit
Reconstruction
Sensors
Sparsity
Standard deviation
strap-down airborne gravimeter
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Summary:Gravity surveys are an important research topic in geophysics and geodynamics. This paper investigates a method for high accuracy large scale gravity anomaly data reconstruction. Based on the airborne gravimetry technology, a flight test was carried out in China with the strap-down airborne gravimeter (SGA-WZ) developed by the Laboratory of Inertial Technology of the National University of Defense Technology. Taking into account the sparsity of airborne gravimetry by the discrete Fourier transform (DFT), this paper proposes a method for gravity anomaly data reconstruction using the theory of compressed sensing (CS). The gravity anomaly data reconstruction is an ill-posed inverse problem, which can be transformed into a sparse optimization problem. This paper uses the zero-norm as the objective function and presents a greedy algorithm called Orthogonal Matching Pursuit (OMP) to solve the corresponding minimization problem. The test results have revealed that the compressed sampling rate is approximately 14%, the standard deviation of the reconstruction error by OMP is 0.03 mGal and the signal-to-noise ratio (SNR) is 56.48 dB. In contrast, the standard deviation of the reconstruction error by the existing nearest-interpolation method (NIPM) is 0.15 mGal and the SNR is 42.29 dB. These results have shown that the OMP algorithm can reconstruct the gravity anomaly data with higher accuracy and fewer measurements.
ISSN:1424-8220
1424-8220
DOI:10.3390/s140305426