Deblending of Seismic Data Based on Neural Network Trained in the CSG
Kunxi Wang, Tianyue Hu
Abstract
The simultaneous source acquisition method, which excites multiple sources in a narrow time interval, can greatly improve the efficiency of seismic data acquisition and provide good illumination. However, the simultaneous source data, also known as the blended data, contain the crosstalk noise from other sources, which brings trouble to the subsequent processing flow. Therefore, an effective deblending method for the simultaneous source data is needed. In order to suppress crosstalk noise, an iterative deblending method using a deep neural network (DNN) trained in the common shot gather (CSG) is developed in this article with the double-blended simultaneous source (DBSS) data being the input data and the blended CSG data being the label data. The proposed training method can not only solve the problem of difficult acquisition of the label data but also make the DNN applicable to any complex formation conditions without considering whether the DNN has the generalization ability of deblending in different work areas. In the test phase, the trained DNN is embedded into the iterative separation framework to deblend the data in the common receiver gather (CRG), which can achieve convergence in a few iterations and achieve a better separation effect. The synthetic and field data examples are tested to verify that the proposed method can effectively suppress the crosstalk noise when deblending the simultaneous source data.