Seismic data reconstruction in Dreamlet domain based on compressive sensing

A new signal processing technique was developed recently due to the progress of compressive sensing theory, which is attracted attention in petroleum and nature gas exploration. Recently great progress has been made in seismic data random sparse sampling, seismic data reconstruction, seismic data regularization, and optimized geometry design. In this paper, we propose a new data reconstruction method based on the data reconstruction method in Dreamlet domain presented by Ru-Shan Wu. According to energy great variation in time and space domain and strong noise of seismic data, we reconstruct and compare synthetic data of random sparse sampling and real seismic data by optimizing parameters and the reconstruction process. Synthetic and real data tests show that the proposed method is a high-efficient and high-quality one. ©, 2015, Science Press. All right reserved.