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Abstract: Deblending is a data processing procedure used to separate the source interferences of blended seismic data, which are obtained by simultaneous sources with random time delays to reduce the cost of seismic acquisition. There are three types of deblending algorithms, i.e., filtering-type noise suppression algorithm, inversion-based algorithm and deep-learning based algorithm. We review the merits of these techniques, and propose to use a sparse inversion method for seismic data deblending. Filtering-based deblending approach is applicable to blended data with a low blending fold and simple geometry. Otherwise, it can suffer from signal distortion and noise leakage. At present, the deep learning based deblending methods are still under development and field data applications are limited due to the lack of high-quality training labels. In contrast, the inversion-based deblending approaches have gained industrial acceptance. Our used inversion approach transforms the pseudo-deblended data into the frequency-wavenumber-wavenumher (FKK) domain, and a sparse constraint is imposed for the coherent signal estimation. The estimated signal is used to predict the interference noise for subtraction from the original pseudo-deblended data. Via minimizing the data misfit, the signal can be iteratively updated with a shrinking threshold until the signal and interference are fully separated. The used FKK sparse inversion algorithm is very accurate and efficient compared with other sparse inversion methods, and it is widely applied in field cases. Synthetic example shows that the deblending error is less than 1% in average amplitudes and less than ?40 dB in amplitude spectra. We present three field data examples of land, marine OBN (Ocean Bottom Nodes) and streamer acquisitions to demonstrate its successful applications in separating the source interferences efficiently and accurately.