<p>Marine simultaneous source seismic acquisition can optimize efficiency and increase data density; however, the recorded data frequently experience signal interference among sources. Effective deblending is essential to suppress blending noise and recover useful signals, ensuring high-quality data processing and analysis. Generally, we use sparse transform techniques to eliminate blending noise. This paper employs the seislet transform, whose prediction operator relies on the local slope of seismic events. The interference from blending noise makes it difficult to forecast the local slope accurately in blended seismic data. We establish an effective two-stage deblending workflow utilizing the seislet transform to resolve this problem. Leveraging the random time delays in simultaneous source acquisition, some segments of the blended seismic data remain unblended. We use this prior information to improve the deblending outcomes. In the first stage, pre-deblending is conducted on the blended data. We subsequently employ the pre-deblended data to inform the prediction of local slopes and construct a mute operator. In the second stage, an iterative deblending framework is utilized within the seislet transform domain, where the seismic event slopes are based on the results predicted in the first stage. The blended seismic data are subjected to the mute operator for each iteration, which separates it into blended and unblended components. Meanwhile, based on the local similarity between recovered useful signals and removed blending noise, we propose an iterative stopping criterion that avoids unnecessary iterations. Tests on model and field data confirm the effectiveness and extensibility of the proposed workflow.</p>