In land oilfields, limited by surface obstacles, conventional seismic data acquisition based on regular sampling theory is becoming more and more difficult to achieve. At the same time, in order to solve more and more complex geological problems, spatial sampling is more and more dense, which resulting in rapid increase seismic exploration costs. In order to adapt to complex surface conditions and save exploration costs, seismic exploration method based on compressed sensing theory was used to investigate, it is concluded that design method about random seismic geometry and uses low-rank constraint algorithms in high-dimensional space to complete the high-density regularization of random seismic data.The effectiveness of this method was proved through theoretical models, The results show that with the same sampling density, this method can obtain better imaging results than regular sampling, and explore a new approach for high-efficiency and high-density seismic exploration in the current eastern old oilfields.