Efficient Unsupervised Deep Learning for Simultaneous Seismic Noise Attenuation and Interpolation

Abstract

We have explored an unsupervised deep learning (DL)-based approach for the efficient and effective reconstruction of noisy and incomplete (N-I) seismic data. This method does not require clean and complete (C-C) seismic data as labeled data. In each iteration, the seismic data input to the network is first subjected to patching techniques, dividing the 2-D or 3-D data into many 1-D signals. Then, to boost the efficiency, a reconstruction error-based patch selection (REBPS) is employed to choose patches that contain more complex structures, which are then fed into the network. The network adopts an encoder and corresponding decoder architecture to compress and reconstruct data features, attenuating noise within the seismic data and performing an initial reconstruction of the missing parts. To improve the reconstruction accuracy, we employ the projection onto convex sets (POCS) algorithm, ultimately obtaining reconstructed data from one iteration. In this process, the output results of each POCS iteration serve as the input for the next round. Through experimental verification using both synthetic and field seismic data, the results show that our proposed method surpasses other comparative methods in the quality of seismic data reconstruction.