Separation and Suppression of Strong Reflections via a Multiscale Attention Deep Learning Model

The existence of coal seams suppresses other useful information, especially the below-thin layers, and is unfavorable for delineating the target reservoirs beneath them. The matching pursuit (MP)-based methods are commonly used for removing strong reflections caused by coal seams. They first decompose a seismic trace into several wavelets based on a user-defined wavelet dictionary and then separate the most similar wavelet with the coal seam. However, how to define a complete wavelet dictionary and how to maintain horizontal continuity are two unsolved issues. We propose a multiscale attention deep learning (MSADL) model for separating and removing seismic strong reflections. First, we suggest a workflow to generate a synthetic dataset for model training based on the characteristics of field data and well logs. Next, we build an MSADL model by integrating the discrete wavelet transform (DWT) and convolutional block attention module (CBAM) into the widely used Unet. After model training, we apply the well-trained MSADL model to 3-D field data in the Sichuan Basin, China for the separation and removal of strong reflections and characterization of the beneath target thin layers.