End-to-End Deblending of Simultaneous Source Data Using Transformer

Abstract

Simultaneous source acquisition is becoming more promising than the traditional seismic acquisition by firing multiple sources with a short interval time, which improves acquisition efficiency and enhances data quality. However, the blended interference severely obscures the coherent signal, challenging the conventional seismic data processing methods. Recently, convolution neural network (CNN) has been successfully implemented to address the blended interference. Different from CNN, the self-attention mechanism based Transformer neural network is good at capturing the global features. In this letter, we propose a Deblending Transformer (DT) based on Transformer module to separate the simultaneous source data. The DT architecture mainly includes linear embedding operation, patch partition based Transformer block and output projection layer. The patch partition algorithm is embedded into the multi-head self-attention module, which extracts the vertical, horizontal and local information. In addition, with the help of linear embedding operation and output projection algorithm, the DT can easily extract the global features from the input. Experiments on synthetic and field data demonstrate that the proposed method has better deblending performance than the U-net based and curvelet based methods.