LAUSR

Absorption attenuation compensation is an important part of seismic data processing. It enhances the resolution of nonstationary seismic data by compensating the amplitude energy and correcting phase distortion. The stabilized inverse $Q$ -filter method, a widely used attenuation compensation method, constructs compensation operators based on stratigraphy-related assumptions and compensates seismic data using time-window analysis, which is computationally complex and sensitive to noise. The essence of attenuation compensation lies in the establishment of a nonlinear mapping relationship between attenuated and nonattenuated seismic traces, which strongly benefits from deep learning. This article proposes a new method for attenuation compensation based on an end-to-end deep neural network to reduce the handcrafted step of time-window analysis. Instead, the convolutional blocks of the network automatically learn and process seismic data features to achieve simultaneous amplitude and phase compensation. We have constructed two end-to-end network architectures for attenuation compensation: a fully convolutional network (FCN) and a U-Net. As an effective spectrum-broadening method, the compensation method based on the U-Net is shown to enhance vertical resolution with good lateral continuity, to provide reliable compensation results without complex calculations, and to exhibit high noise robustness. Synthetic data tests indicate that the compensation results from the U-Net are better than those from either the FCN or the stabilized inverse $Q$ -filter method at different noise levels. Moreover, the correlation coefficient (CC) between the U-Net compensation results of the synthetic profile and the reference nonattenuated profile is higher than that of the FCN and the stabilized inverse $Q$ -filter method. A field data application further verifies the effectiveness of this method.