Effective random noise attenuation is critical for subsequent processing of seismic data,
such as velocity analysis, migration, and inversion. Thus, the removal of seismic random …

卷积神经网络(Convolutional Neural Network, CNN) 是一种基于数据驱动的学习算法,
简化了传统从特征提取到分类的两阶段式处理任务, 被广泛应用于计算机科学的各个领域 …

Deep learning has been successfully applied to image denoising. In this study, we take one
step forward by using deep learning to suppress random noise in poststack seismic data …

Because a high signal-to-noise ratio (SNR) is beneficial to the subsequent processing
procedures, the noise attenuation is important. We propose an adaptive random noise …

As a major concern, the existence of unwanted energy and missing traces in seismic data
acquisition can degrade interpretation of such data after processing. Instead of analytical …

Seismic data comprise many traces that provide a spatiotemporal sampling of the reflected
wavefield. However, such information may suffer from ambient and random noise during …

Convolutional neural network (CNN) has been widely adopted in various research fields of
computer science. Combining the process of feature extracting and classification, CNN …

Traditional supervised denoising networks learn network weights through “black box”(pixel-
oriented) training, which requires clean training labels. The inability of such denoising …

We have trained a supervised deep 3D convolutional neural network (CNN) on marine
seismic images for poststack structural seismic image enhancement and noise attenuation …

Random noise attenuation has always been an indispensable step in the seismic
exploration workflow. The quality of the results directly affects the results of subsequent …