Low-frequency Expansion Based on Compressed Sensing

The use of low-frequency seismic data improves the seismic resolution, as well as the imaging and inversion quality. Furthermore, low-frequency data is directly applied in hydrocarbon exploration, thus, we need to take a better advantage of low-frequency data. In this paper, we apply compressed sensing theory to real seismic data. With the lost low-frequency seismic date, we have performed low-frequency expansion based on compressed sensing and sparse constraints, meanwhile, we also develop the corresponding module. Finally, we apply the proposed method to real common image point gathers, expanding the range of the low-frequency for the seismic data in a reasonable manner, so as to improve the quality of the data and the accuracy of the inversion and interpretation, which has got the good performance.