基于深度学习的地震偏移噪声压制研究

Research on noise suppression of seismic migration based on deep learning

高信噪比成像对于油气勘探具有重要意义,压制偏移产生的噪声可以提高地震成像的信噪比.本文提出了一个基于卷积神经网络压制地震偏移噪声的方法,网络结构主要包括编码器和解码器,编码器用于提取特征,解码器用于恢复图像,该方法通过直接对地震剖面学习实现了地震偏移噪声压制的自动化.实际数据的实验结果验证了本文方法的有效性.本文的方法不仅可以保留地震剖面的主要特征,而且有效的压制了偏移噪声,对提高地震剖面信噪比和提高地震数据处理的效率具有重要意义.

High signal-to-noise ratio imaging is of great significance to oil and gas exploration.Suppressing the migration noise can improve the signal-to-noise ratio of seismic imaging.This paper proposes a method based on convolutional neural network to suppress seismic migration noise.The network structure mainly includes an encoder and a decoder.The encoder is used to extract features,and the decoder is used to restore the image.This method realizes the automation of seismic migration noise suppression by directly learning the seismic profile.The experimental results of real data verify the effectiveness of the method in this paper.The method in this paper can not only retain the main characteristics of the seismic profile,but also effectively suppress the migration noise,which is of great significance for improving the signal-to-noise ratio of the seismic profile and improving the efficiency of seismic data processing.