Blended Noise Suppression Using a Hybrid Median Filter, Normal Moveout and Complex Curvelet Transform Approach

The high density acquisition way can uplift the subsurface imaging accuracy, whereas the high cost limits the widely application in practice. Blending acquisition way has emerged as a promising way of significantly increasing the efficiency of seismic acquisition. However, there will exist a large challenge of severe interference noise and decrease S/N ratio. Therefore, with recent processing practices, the success of blending acquisition relies heavily on the effectiveness of de-blending to separate signals from simultaneous sources. In the paper, we proposed a blended noise suppression approach using a hybrid median filter, normal moveout (NMO) and complex curvelet transform (CCT) approach. Firstly, the large step median filter is applied to the initial data after NMO correction. Next, we continue to extract the residual energy to get the de-blended result by the CCT-based threshold method. Then, re-iterate the difference data by subtracting the original pseudo de-blended data and the pseudo de-blended data of the de-blended result from each iteration as the above processing flow. Finally, the final de-blended data is derived by adding the remained energy of each iteration until the S/N ratio satisfies the desired one. We demonstrate through a simulated field data the effectiveness of the approach.